Online recruitment platforms have reshaped job-seeking and recruiting processes, driving increased demand for applications that enhance person-job matching. Traditional methods generally rely on analyzing textual data from resumes and job descriptions, limiting the dynamic, interactive aspects crucial to effective recruitment. Recent advances in Large Language Models (LLMs) have revealed remarkable potential in simulating adaptive, role-based dialogues, making them well-suited for recruitment scenarios. In this paper, we propose MockLLM, a novel framework to generate and evaluate mock interview interactions. The system consists of two key components: mock interview generation and two-sided evaluation in handshake protocol. By simulating both interviewer and candidate roles, MockLLM enables consistent and collaborative interactions for real-time and two-sided matching. To further improve the matching quality, MockLLM further incorporates reflection memory generation and dynamic strategy modification, refining behaviors based on previous experience. We evaluate MockLLM on real-world data Boss Zhipin, a major Chinese recruitment platform. The experimental results indicate that MockLLM outperforms existing methods in matching accuracy, scalability, and adaptability across job domains, highlighting its potential to advance candidate assessment and online recruitment.

Mobile phone agents can assist people in automating daily tasks on their phones, which have emerged as a pivotal research spotlight. However, existing procedure-oriented agents struggle with cross-app instructions, due to the following challenges: (1) complex task relationships, (2) diverse app environment, and (3) error propagation and information loss in multi-step execution. Drawing inspiration from object-oriented programming principles, we recognize that object-oriented solutions is more suitable for cross-app instruction. To address these challenges, we propose a self-evolving multi-agent framework named MobileSteward which integrates multiple app-oriented StaffAgents coordinated by a centralized StewardAgent. We design three specialized modules in MobileSteward: (1) Dynamic Recruitment generates a scheduling graph guided by information flow to explicitly associate tasks among apps. (2) Assigned Execution assigns the task to app-oriented StaffAgents, each equipped with app-specialized expertise to address the diversity between apps. (3) Adjusted Evaluation conducts evaluation to provide reflection tips or deliver key information, which alleviates error propagation and information loss during multi-step execution. To continuously improve the performance of MobileSteward, we develop a Memory-based Self-evolution mechanism, which summarizes the experience from successful execution, to improve the performance of MobileSteward. We establish the first English Cross-APP Benchmark (CAPBench) in the real-world environment to evaluate the agents' capabilities of solving complex cross-app instructions. Experimental results demonstrate that MobileSteward achieves the best performance compared to both single-agent and multi-agent frameworks, highlighting the superiority of MobileSteward in better handling user instructions with diverse complexity.

In today's digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditional classification models, which suffer from low interpretability and limited generalization capabilities, or craft specific prompts for large language models (LLMs) to produce explanations and results directly, failing to leverage LLMs' reasoning abilities fully. Inspired by the saying that ''truth becomes clearer through debate,'' our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. TED employs a rigorous debate process inspired by formal debate settings. Central to our approach are two innovative components: the DebateFlow Agents and the InsightFlow Agents. The DebateFlow Agents organize agents into two teams, where one supports and the other challenges the truth of the news. These agents engage in opening statements, cross-examination, rebuttal, and closing statements, simulating a rigorous debate process akin to human discourse analysis, allowing for a thorough evaluation of news content. Concurrently, the InsightFlow Agents consist of two specialized sub-agents: the Synthesis Agent and the Analysis Agent. The Synthesis Agent summarizes the debates and provides an overarching viewpoint, ensuring a coherent and comprehensive evaluation. The Analysis Agent, which includes a role-aware encoder and a debate graph, integrates role embeddings and models the interactions between debate roles and arguments using an attention mechanism, providing the final judgment.Our extensive experiments on two datasets, ARG-EN and ARG-CN, demonstrate that the TED framework surpasses traditional methods across various metrics and, more importantly, enhances interpretable fake news detection by illuminating logical reasoning and structured debate processes leading to accurate conclusions.We release our code to support Information systems that use structured debate within responsible information systems for improved decision-making.

Influencer marketing campaign success heavily depends on identifying key opinion leaders who can effectively leverage their credibility and reach to promote products or services.The selection of influencers is vital for boosting brand visibility, fostering consumer trust, and driving sales.While traditional research often simplifies complex factors like user attitudes, interaction frequency, and advertising content, into simple numerical values.However, this reductionist approach fails to capture the dynamic nature of influencer marketing effectiveness.To bridge this gap, we present SAGraph, a novel comprehensive dataset from Weibo that captures multi-dimensional marketing campaign data across six product domains.The dataset encompasses 345,039 user profiles with their complete interaction histories, including 1.3M comments and 554K reposts across 44K posts, providing unprecedented granularity in influencer marketing dynamics.SAGraph uniquely integrates user profiles, content features, and temporal interaction patterns, enabling in-depth analysis of influencer marketing mechanisms.Experimental results using both traditional baselines and state-of-the-art large language models (LLMs) demonstrate the crucial role of content analysis in predicting advertising effectiveness.Our findings reveal that LLM-based approaches achieve superior performance in understanding and predicting campaign success, opening new avenues for data-driven influencer marketing strategies.We hope that this dataset will inspire further research: https

Tables are ubiquitous across various domains for concisely representing structured information. Empowering large language models (LLMs) to reason over tabular data represents an actively explored direction. However, since typical LLMs only support one-dimensional (1D) inputs, existing methods often flatten the two-dimensional (2D) table structure into a sequence of tokens, which can severely disrupt the spatial relationships and result in an inevitable loss of vital contextual information. In this paper, we first empirically demonstrate the detrimental impact of such flattening operations on the performance of LLMs in capturing the spatial information of tables through two elaborate proxy tasks. Subsequently, we introduce a simple yet effective positional encoding method, termed "2D-TPE" (Two-Dimensional Table Positional Encoding), to address this challenge. 2D-TPE enables each attention head to dynamically select a permutation order of tokens within the context for attending to them, where each permutation represents a distinct traversal mode for the table, such as column-wise or row-wise traversal. 2D-TPE effectively mitigates the risk of losing essential spatial information while preserving computational efficiency, thus better preserving the table structure. Extensive experiments across five benchmarks demonstrate that 2D-TPE outperforms strong baselines, underscoring the importance of preserving the table structure for accurate table comprehension. Comprehensive analysis further reveals the substantially better scalability of 2D-TPE to large tables than baselines.

Large language models (LLMs) enhanced with retrieval-augmented generation (RAG) have introduced a new paradigm for web search. However, the limited context awareness of LLMs degrades their performance on RAG tasks. Existing methods to enhance context awareness are often inefficient, incurring time or memory overhead during inference, and many are tailored to specific position embeddings. In this paper, we propose Position-Embedding-Agnostic attention Re-weighting (PEAR), which enhances the context awareness of LLMs with zero inference overhead. Specifically, on a proxy task focused on context copying, we first detect heads which suppress the models' context awareness, thereby diminishing RAG performance. To weaken the impact of these heads, we re-weight their outputs with learnable coefficients. The LLM (with frozen parameters) is optimized by adjusting these coefficients to minimize loss on the proxy task. During inference, the optimized coefficients are fixed to re-weight these heads, regardless of the specific task at hand. Our proposed PEAR offers two major advantages over previous approaches: (1) It introduces zero additional inference overhead in terms of memory usage or inference time, while outperforming competitive baselines in accuracy and efficiency across various RAG tasks. (2) It is independent of position embedding algorithms, ensuring broader applicability. Our code is available at https://github.com/TTArch/PEAR-RAG.

Quantitative trading is a prominent field that employs time series analysis today, attracting researchers who apply machine intelligence to real-world issues like stock price movement prediction. In recent literature, various types of auxiliary data have been integrated alongside stock prices to improve prediction accuracy, such as textual news and correlational information. However, they typically rely on directly related documents or symmetric price correlations to make predictions for a particular stock (we refer to as ” self-influence”). In this paper, we propose a Memory-Aware Graph Interactive Causal Network (MagicNet) that considers both temporal and spatial dependencies in financial documents and introduces causality-based correlations between multivariate stocks in a hierarchical fashion. MagicNet involves a text memory slot for each stock to retain the most influential texts over time and contains a dynamic interaction graph based on causal relationships to aggregate interactive influences asymmetrically. We believe that MagicNet leverages influential texts across stocks and explores their interrelationships through a logical structure, improving predictions on multiple stocks (we refer to as ”interactive-influence”). The effectiveness of MagicNet is demonstrated through experiments on three real-world datasets, where MagicNet outperforms existing state-of-the-art models, offering an intuitive framework for understanding how texts and correlations affect future stock prices.

Review summarization aims to provide a summary that covers the main aspect of the product review and reflects personal preference. Existing methods employ the historical reviews of customer and product to provide useful clues for the target summary generation. However, most of the existing methods indiscriminately model the historical reviews of customer and product. Since the historicalcustomerreviews provide the personal information while the historicalproductreviews provide the commonly focused aspect of the product, these two types of heterogeneous information should be separately modeled. Moreover, the review rating of the historical reviews can be seen as a high-level abstraction of the customer preference and product which have been ignored by most of the existing methods. In this paper, we propose the Heterogeneous Historical Review aware Review Summarization (HHRRS) which separately models the two types of historical reviews with the rating information by a graph reasoning module with a contrastive loss. We employ a multi-task paradigm that conducts the review sentiment classification and summarization (GRARS) to model the two types of heterogeneous information in a fine-grained manner. We conduct extensive experiments on four benchmark datasets, and demonstrate the superiority of HHRRS on both tasks.

Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the application of deep learning methods to DTA prediction, the achieved accuracy remain suboptimal. In this work, inspired by the recent success of retrieval methods, we propose kNN-DTA, a non-parametric embedding-based retrieval method adopted on a pre-trained DTA prediction model, which can extend the power of the DTA model with no or negligible cost. Different from existing methods, we introduce two neighbor aggregation ways from both embedding space and label space that are integrated into a unified framework. Specifically, we propose a label aggregation with pair-wise retrieval and a representation aggregation with point-wise retrieval of the nearest neighbors. This method executes in the inference phase and can efficiently boost the DTA prediction performance with no training cost. In addition, we propose an extension, Ada-kNN-DTA, an instance-wise and adaptive aggregation with lightweight learning. Results on four benchmark datasets show that kNN-DTA brings significant improvements, outperforming previous state-of-the-art (SOTA) results, e.g, on BindingDB IC50 and Ki testbeds, kNN-DTA obtains new records of RMSE 0.684 and 0.750 . The extended Ada-kNN-DTA further improves the performance to be 0.675 and 0.735 RMSE. These results strongly prove the effectiveness of our method. Results in other settings and comprehensive studies/analyses also show the great potential of our kNN-DTA approach.

In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedback behaviors. Leveraging the session context has proven to be beneficial for inferring user search intent and document ranking. A multitude of approaches have been proposed to exploit in-session context for improved document ranking. Despite these advances, the limitation of historical session data for capturing evolving user intent remains a challenge. In this work, we explore the integration of future contextual information into the session context to enhance document ranking. We present the siamese model optimization framework, comprising a history-conditioned model and a future-aware model. The former processes only the historical behavior sequence, while the latter integrates both historical and anticipated future behaviors. Both models are trained collaboratively using the supervised labels and pseudo labels predicted by the other. The history-conditioned model, referred to as ForeRanker, progressively learns future-relevant information to enhance ranking, while it singly uses historical session at inference time. To mitigate inconsistencies during training, we introduce the peer knowledge distillation method with a dynamic gating mechanism, allowing models to selectively incorporate contextual information. Experimental results on benchmark datasets demonstrate the effectiveness of our ForeRanker, showcasing its superior performance compared to existing methods.

A proficient summarization model should exhibit both flexibility -- the capacity to handle a range of in-domain summarization tasks, and adaptability -- the competence to acquire new knowledge and adjust to unseen out-of-domain tasks. Unlike large language models (LLMs) that achieve this through parameter scaling, we propose a more parameter-efficient approach in this study. Our motivation rests on the principle that the general summarization ability to capture salient information can be shared across different tasks, while the domain-specific summarization abilities need to be distinct and tailored. Concretely, we propose MoeSumm, a Mixture-of-Expert Summarization architecture, which utilizes a main expert for gaining the general summarization capability and deputy experts that selectively collaborate to meet specific summarization task requirements. We further propose a max-margin loss to stimulate the separation of these abilities. Our model's distinct separation of general and domain-specific summarization abilities grants it with notable flexibility and adaptability, all while maintaining parameter efficiency. MoeSumm achieves flexibility by managing summarization across multiple domains with a single model, utilizing a shared main expert and selected deputy experts. It exhibits adaptability by tailoring deputy experts to cater to out-of-domain few-shot and zero-shot scenarios. Experimental results on 11 datasets show the superiority of our model compared with recent baselines and LLMs. We also provide statistical and visual evidence of the distinct separation of the two abilities in MoeSumm https://github.com/iriscxy/MoE_Summ

The development of AI in mental health is a growing field with potential global impact. Machine agents need to perceive users' mental states and respond empathically. Since mental states are often latent and implicit, building such chatbots requires both knowledge learning and knowledge utilization. Our work contributes to this by developing a chatbot that aims to recognize and empathetically respond to users' mental states. We introduce a Conditional Variational Autoencoders (CVAE)-based model that utilizes relation-aware commonsense knowledge to generate responses. This model, while not a replacement for professional mental health support, demonstrates promise in offering informative and empathetic interactions in a controlled environment. On the dataset EmpatheticDialogues, we compare with several SOTA methods and empirically validate the effectiveness of our approach on response informativeness and empathy exhibition. Detailed analysis is also given to demonstrate the learning capability as well as model interpretability. Our code is accessible at http://github.com/ChangyuChen347/COMET-VAE.

Knowledge Graph Embedding (KGE) is a critical field aiming to transform the elements of knowledge graphs (KGs) into continuous spaces, offering great potential for structured data representation. In contemporary KGE research, the utilization of either hyperbolic or Euclidean space for knowledge graph Embedding is a common practice. However, knowledge graphs encompass diverse geometric data structures, including chains and hierarchies, whose hybrid nature exceeds the capacity of a single embedding space to capture effectively. This paper introduces a novel and highly effective approach called Unified Geometry Knowledge Graph Embedding (UniGE) to address the challenge of representing diverse geometric data in KGs. UniGE stands out as a novel KGE method that seamlessly integrates KGE in both Euclidean and hyperbolic geometric spaces. We introduce an embedding alignment method and fusion strategy, which harnesses optimal transport techniques and the Wasserstein barycenter method. Furthermore, we offer a comprehensive theoretical analysis to substantiate the superiority of our approach, as evident from a more robust error bound. To substantiate the strength of UniGE, we conducted comprehensive experiments on three benchmark datasets. The results consistently demonstrate that UniGE outperforms state-of-the-art methods, aligning with the conclusions drawn from our theoretical analysis.

Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. Existing methods follow two main paradigms to collect evidence: (1) Theretrieve-then-read paradigm retrieves pertinent documents from an external corpus; and (2) thegenerate-then-read paradigm employs large language models (LLMs) to generate relevant documents. However, neither can fully address multifaceted requirements for evidence. To this end, we propose LLMQA, a generalized framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation, combining the superiority of both retrieval-based and generation-based evidence. Since LLMs exhibit their excellent capabilities to accomplish various tasks, we instruct LLMs to play multiple roles as generators, rerankers, and evaluators within our framework, integrating them to collaborate in the ODQA process. Furthermore, we introduce a novel prompt optimization algorithm to refine role-playing prompts and steer LLMs to produce higher-quality evidence and answers. Extensive experimental results on widely used benchmarks (NQ, WebQ, and TriviaQA) demonstrate that LLMQA achieves the best performance in terms of both answer accuracy and evidence quality, showcasing its potential for advancing ODQA research and applications.

Session search involves a series of interactive queries and actions to fulfill user's complex information need. Current strategies typically prioritize sequential modeling for deep semantic understanding, overlooking the graph structure in interactions. While some approaches focus on capturing structural information, they use a generalized representation for documents, neglecting the word-level semantic modeling. In this paper, we propose Symbolic Graph Ranker (SGR), which aims to take advantage of both text-based and graph-based approaches by leveraging the power of recent Large Language Models (LLMs). Concretely, we first introduce a set of symbolic grammar rules to convert session graph into text. This allows integrating session history, interaction process, and task instruction seamlessly as inputs for the LLM. Moreover, given the natural discrepancy between LLMs pre-trained on textual corpora, and the symbolic language we produce using our graph-to-text grammar, our objective is to enhance LLMs' ability to capture graph structures within a textual format. To achieve this, we introduce a set of self-supervised symbolic learning tasks including link prediction, node content generation, and generative contrastive learning, to enable LLMs to capture the topological information from coarse-grained to fine-grained. Experiment results and comprehensive analysis on two benchmark datasets, AOL and Tiangong-ST, confirm the superiority of our approach. Our paradigm also offers a novel and effective methodology that bridges the gap between traditional search strategies and modern LLMs.

Retrieval-based dialogue systems are a crucial component of natural language processing, employing information retrieval techniques to select responses from a predefined pool of candidates. The advent of pre-trained language models (PLMs) has significantly advanced the field, with a prevailing paradigm that involves post-training PLMs on specific dialogue corpora, followed by fine-tuning for the response selection (RS) task. This post-training process aims to capture dialogue-specific features, as most PLMs are originally trained on plain text. However, prior approaches predominantly rely on self-supervised tasks or session-level graph neural networks during post-training, focusing on capturing underlying patterns of coherent dialogues without explicitly refining the global pattern across the entire dialogue corpus. Consequently, the learned knowledge for organizing coherent dialogues remains isolated, heavily reliant on specific contexts. Additionally, interpreting or visualizing the implicit knowledge acquired through self-supervised tasks proves challenging. In this study, we address these limitations by explicitly refining the knowledge required for response selection and structuring it into a coherent global flow, known as "dialogue structure." This structure captures the inter-dependency of utterances and topic shifts, thereby enhancing the response selection task. To achieve this, we propose a novel structure model comprising a state recognizer and a structure planner. This model effectively captures the flow within the utterance history and plans the trajectory of future utterances. Importantly, the structure model operates orthogonally to the retrieval model, enabling seamless integration with existing retrieval models and facilitating collaborative training. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our method over a wide range of competitive baselines, establishing a new state-of-the-art in the field.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which make it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Specifically, to keep the dialog on track for professional interviews, we pre-train a knowledge selector module to extract information from resume in the job-resume matching. A dialog generator is also pre-trained with ungrounded dialogs, learning to generate fluent responses. Then, a decoding manager is finetuned to combine information from the two pre-trained modules to generate the interview question. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dialogue summarization, which aims to generate a summary for an input dialogue, plays a vital role in intelligent dialogue systems. The end-to-end models have achieved satisfactory performance in summarization, but the success is built upon enough annotated data, which is costly to obtain, especially in the dialogue summarization. To leverage the rich external data, previous works first pre-train the model on the other domain data (e.g., the news domain), and then fine-tune it directly on the dialogue domain. The data from different domains are equally treated during the training process, while the vast differences between dialogues (usually informal, repetitive, and with multiple speakers) and conventional articles (usually formal and concise) are neglected. In this work, we propose to use a disentangled representation method to reduce the deviation between data in different domains, where the input data is disentangled into domain-invariant and domain-specific representations. The domain-invariant representation carries context information that is supposed to be the same across domains (e.g., news, dialogue) and the domain-specific representation indicates the input data belongs to a particular domain. We use adversarial learning and contrastive learning to constrain the disentangled representations to the target space. Furthermore, we propose two novel reconstruction strategies, namely backtracked and cross-track reconstructions, which aim to reduce the domain characteristics of out-of-domain data and mitigate the domain bias of the model. Experimental results on three public datasets show that our model significantly outperforms the strong baselines.

Publishing the research works on academic publications is an important part of the scientific process. Since the development of computer science research is very fast, researchers tend to publish the research works in a fast way, such as conferences whose review processes are faster than the journals. In the past decades, one conference usually focuses on a specific research field and the topic or method overlap between conferences is low. We have noticed that, in recent years, some topics or methods which were once studied in a small number of specific research fields have become popular in many other fields. Naturally, we come up with two research questions: (1) Do the conferences indeed become similar? and (2) How do conferences become similar? In this paper, we first use a trans-temporal heterogeneous graph network to model academic conferences in recent 20 years. Due to the large number of conferences, we categorize these conferences into 6 research fields for brevity. Then, we first quantitatively and qualitatively assess “Do the research fields become similar?” and then focus on exploring “How do research fields become similar?”. From the result, we find the reason for the research fields in computer science become similar is that AI becomes pervasive and researchers tend to apply the machine learning methods to different application fields. Since the methods become universal between different research fields, researchers should pay more attention to advanced information in other fields to motivate more interdisciplinary works. To assist the researchers to explore related interdisciplinary advanced information, it is crucial to measure the cross-field impact of papers using the citation information and recommend the paper which has a high cross-field impact on the related researchers. As for the newly published papers which do not have any citations, we also propose a cross-field impact prediction model to recommend the cutting-edge research works to related researchers accurately. Experiments conducted on real-world datasets verify the effectiveness of the proposed method.

Today, timestamped web documents related to a general news query flood the Internet, and timeline summarization targets this concisely by summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this article we propose our Unified Timeline Summarizer, which can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information retained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting a summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that our Unified Timeline Summarizer achieves state-of-the-art performance in terms of both automatic and human evaluations. 1

Employing query suggestion techniques to assist users in articulating their needs during online search has become increasingly vital for search engines in an age of exponential information growth. The success of a query suggestion system lies in understanding and modeling user search intent behind each query accurately, which can hardly be achieved without personalization efforts on taking advantage of dynamic user feedback behaviors and rich contextual information. This valuable area, however, has been still largely untapped by current query suggestion systems. In this work, we propose Dynamic Searching Flow Model (DSFM), a query suggestion framework that is capable of modeling and refining user search intent progressively in recruitment scenarios by leveraging a dynamic flow mechanism. Here the concepts of local flow and global flow are introduced to capture the real-time intention of users and the overall influence of a session, respectively. By utilizing rich semantic information contained in resumes and job requirements, DSFM enables the personalization of query suggestions. In addition, weighted contrast learning is introduced into the training process to produce more extensive targeted query samples and partially alleviate the exposure bias. The adoption of attention mechanism allows the selection of the most relevant information to compose the final intention representation. Extensive experimental results on different categories of real-world datasets demonstrate the effectiveness of our proposed approach on the task of query suggestion for online recruitment platforms.

Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for retrosynthetic planning. We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo tree search). Such redundancies make the search process inefficient. We propose a graph-based search policy that eliminates the redundant explorations of any intermediate molecules. As searching over a graph is more complicated than over a tree, we further adopt a graph neural network to guide the search over graphs. Meanwhile, our method can search a batch of targets together in the graph and remove the inter-target duplication in the tree-based search methods. Experimental results on two datasets demonstrate the effectiveness of our method. Especially on the widely used USPTO benchmark, we improve the search success rate to 99.47%, advancing previous state-of-the-art performance for 2.6 points.

Chit-chat has been shown effective in engaging users in human-computer interaction. We find with a user study that generating appropriate chit-chat for news articles can help expand user interest and increase the probability that a user reads a recommended news article. Based on this observation, we propose a method to generate personalized chit-chat for news recommendation. Different from existing methods for personalized text generation, our method only requires an external chat corpus obtained from an online forum, which can be disconnected from the recommendation dataset from both the user and item (news) perspectives. This is achieved by designing a weak supervision method for estimating users' personalized interest in a chit-chat post by transferring knowledge learned by a news recommendation model. Based on the method for estimating user interest, a reinforcement learning framework is proposed to generate personalized chit-chat. Extensive experiments, including the automatic offline evaluation and user studies, demonstrate the effectiveness of our method.

The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Authors can save their time and effort by using the automatically generated related work section as a draft to complete the final related work. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences to make a comparative discussion about the target work and the reference papers. However, such sentences need to be written in advance and are hard to obtain in practice. Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. Concretely, we first propose a target-aware graph encoder, which models the relationships between reference papers and the target paper with target-centered attention mechanisms. In the decoding process, we propose a hierarchical decoder that attends to the nodes of different levels in the graph with keyphrases as semantic indicators. Finally, to generate a more informative related work, we propose multi-level contrastive optimization objectives, which aim to maximize the mutual information between the generated related work with the references and minimize that with non-references. Extensive experiments on two public scholar datasets show that the proposed model brings substantial improvements over several strong baselines in terms of automatic and tailored human evaluations.

Conversational recommendation system aims to recommend appropriate items to user by directly asking preference on attributes or recommending item list. However, most of existing methods only employ the flat item and attribute relationship, and ignore the hierarchical relationship connected by the similar user which can provide more comprehensive information. And these methods usually use the user accepted attributes to represent the conversational history and ignore the hierarchical information of sequential transition in the historical turns. In this paper, we propose Hierarchical Information-aware Conversational Recommender (HICR) to model the two types of hierarchical information to boost the performance of CRS. Experiments conducted on four benchmark datasets verify the effectiveness of our proposed model.

Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. In these communities, users can post articles, give comment, raise a question and answer it. These data form the heterogeneous information sources where each information source have their own special structure and context (comments attached to an article or related question with answers). Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. However, various types of information sources in the community are not fully explored by these CQA methods and these multiple information sources (MIS) can provide more related knowledge to user's questions. Thus, we propose a question-aware heterogeneous graph transformer to incorporate the MIS in the user community to automatically generate the answer. To evaluate our proposed method, we conduct the experiments on two datasets: $\textMSM ^\textplus $ the modified version of benchmark dataset MS-MARCO and the AntQA dataset which is the first large-scale CQA dataset with four types of MIS. Extensive experiments on two datasets show that our model outperforms all the baselines in terms of all the metrics.

With the outbreak of COVID-19, it is urgent and necessary to design a system that can access to information from COVID-19 related documents. Current methods fail to do so since the knowledge about COVID-19, an emerging disease, keeps changing and growing. In this study, we design a dynamic document-based question answering system, namely Web Understanding and Learning with AI (WULAI-QA). WULAI-QA employs feature engineering and online learning to adapt to the non-stationary environment and maintains good and steady performance. We evaluate WULAI-QA's performance on a public question answering (https://www.datafountain.cn/competitions/424) and rank first. We demonstrate that WULAI-QA can learn from user feedback and is easy to use. We believe that WULAI-QA will definitely help people understand COVID-19 and play an important role to fight against the pandemic.

Since late December 2019, it has been reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in-person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train multilingual question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching the stickers image with previous utterances. However, existing methods usually focus on measuring the matching degree between the dialog context and sticker image, which ignores the user preference of using stickers. Hence, in this article, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user. Two main challenges are confronted in this task. One is to model the sticker preference of user based on the previous sticker selection history. Another challenge is to jointly fuse the user preference and the matching between dialog context and candidate sticker into final prediction making. To tackle these challenges, we propose a Preference Enhanced Sticker Response Selector (PESRS) model. Specifically, PESRS first employs a convolutional-based sticker image encoder and a self-attention-based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker and each utterance. Then, we model the user preference by using the recently selected stickers as input and use a key-value memory network to store the preference representation. PESRS then learns the short-term and long-term dependency between all interaction results by a fusion network and dynamically fuses the user preference representation into the final sticker selection prediction. Extensive experiments conducted on a large-scale real-world dialog dataset show that our model achieves the state-of-the-art performance for all commonly used metrics. Experiments also verify the effectiveness of each component of PESRS.

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task ofproduct-aware answer generation, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However,safe answer problems(i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called theMeaningful Product Answer Generator(MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conductreasoning among these reviews. We then employ a prototype reader consisting of comprehensive matching to extract theanswer skeletonfrom the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: (1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; (2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation [ K ] -interaction [ L ] -matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance–response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context–response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.

Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from prominent weakness for conversations: the system finds similar responses given a particular query. For real human-to-human conversations, on the contrary, responses can be greatly different yet all are possibly appropriate. The observation reveals the diversity phenomenon in conversations. In this article, we ascribe the lack of conversational diversity to the reason that the query utterances are statically modeled regardless of candidate responses through traditional methods. To this end, we propose a dynamic representation learning strategy that models the query utterances and different response candidates in an interactive way. To be more specific, we propose a Respond-with-Diversity model augmented by the memory module interacting with both the query utterances and multiple candidate responses. Hence, we obtain dynamic representations for the input queries conditioned on different response candidates. We frame the model as an end-to-end learnable neural network. In the experiments, we demonstrate the effectiveness of the proposed model by achieving a good appropriateness score and much better diversity in retrieval-based conversations between humans and computers.

Given a user query, traditional multi-turn retrieval-based dialogue systems first retrieve a set of candidate responses from the historical dialogue sessions. Then the response selection models select the most appropriate response to the given query. However, previous work only considers the matching between the query and the response but ignores the informative dialogue session in which the response is located. Nevertheless, this session, composed of the response, the response's history and the response's future, always contains valuable contextual information which can help the response selection task. More specifically, if the current query and a response's history both refer to the same question, we can conclude that this response is quite likely to answer this query. As for the response's future, it can always provide contextual hints and supplementary information that might be omitted in the response. Inspired by such motivation, we propose a query-to-session matching (QSM) framework to make full use of the session information: matching the query with the candidate session instead of the response only. Different from the previous work which ranks response directly, the response in the session with the highest query-to-session matching score will be selected as the desired response. In our proposed framework, the query, history, and future are all sequences of utterances, which makes it necessary to model the relationships among the utterances. So we propose a novel dialogue flow aware query-to-session matching (DF-QSM) model. The dialogue flows model the relationships among the utterances through a memory network. To our best knowledge, our paper is the first work to utilize both the response's history and future in the response selection task. The experimental results on three multi-turn response selection benchmarks show that our proposed model outperforms existing state-of-the-art methods by a large margin.

Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts to utilize neural networks to build a matching model, where all of the context and knowledge contents are used to match the response candidate with various representation methods.

We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of the previous works select the response according to the matching degree between the query's context and the candidate responses. Though great progress has been made, existing works ignore the contexts of the responses, which could provide rich information for selecting the most appropriate response. The more similar the query's context and certain response's context are, the more likely they are to indicate the same question, and thus, the more likely this response is to answer the query. In this paper, we consider the response and its context as a whole session and explore the task of matching the query's context with the sessions. More specifically, we propose to match between the query's context and response's context and integrate the context-to-context matching with context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.

Dating back from late December 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia, now known as lung inflammation caused by novel coronavirus (COVID-19). Cases have spread to other cities in China and more than 180 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency is perhaps one of the top concerns in the year of 2020 for governments all over the world. Till today, the coronavirus outbreak is still raging and has no sign of being under control in many countries. In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need. To this end, we have built a system predicting hazard areas on the basis of confirmed infection cases with location information. The purpose is to warn people to avoid of such hot zones and reduce risks of disease transmission through droplets or contacts. We analyze the data from the daily official information release which are publicly accessible. Based on standard classification frameworks with reinforcements incrementally learned day after day, we manage to conduct thorough feature engineering from empirical studies, including geographical, demographic, temporal, statistical, and epidemiological features. Compared with heuristics baselines, our method has achieved promising overall performance in terms of precision, recall, accuracy, F1 score, and AUC. We expect that our efforts could be of help in the battle against the virus, the common opponent of human kind.

For social bots, smooth emotional transitions are essential for delivering a genuine conversation experience to users. Yet, the task is challenging because emotion is too implicit and complicated to understand. Among previous studies in the domain of retrieval-based conversational model, they only consider the factors of semantic and functional dependencies of utterances. In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion. Extensive experiments on two benchmark datasets indicate that the proposed model can effectively track the flow of emotions throughout a human-machine conversation and significantly improve response selection in dialogues over the state-of-the-art baselines. We also empirically validate the emotion-control effects of our proposed model on three different emotional aspects. Finally, we apply such functionalities to a real IoT application.

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots. Existing models, such as Cove and ELMo, are trained with limited context (often a single sentence or paragraph), and may not work well on multi-turn conversations, due to the hierarchical nature, informal language, and domain-specific words. To address the challenges, we propose pre-training hierarchical contextualized representations, including contextual word-level and sentence-level representations, by learning a dialogue generation model from large-scale conversations with a hierarchical encoder-decoder architecture. Then the two levels of representations are blended into the input and output layer of a matching model respectively. Experimental results on two benchmark conversation datasets indicate that the proposed hierarchical contextualized representations can bring significantly and consistently improvement to existing matching models for response selection.

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale real-world dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs1.

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.

The diversity of job requirements and the complexity of job seekers' abilities put forward higher requirements for the accuracy and interpretability of Person-Job Fit system. Interpretable Person-Job Fit system can show reasons for giving recommendations or not recommending specific jobs to some people, and vice versa. Such reasons help us understand according to what the final decision is made by the system and guarantee a high recommending accuracy. Existing studies on Person-Job Fit have focused on 1) one perspective, without considering the variances of role and psychological motivation between interviewer and job seeker; 2) modeling the matching degree between resume and job requirements directly through a deep neural network without interaction matching modules, which leads to shortage on interpretation. To this end, we propose an Interpretable Person-Job Fit (IPJF) model, which 1) models the Person-Job Fit problem from the perspectives/intentions of employer and job seeker in a multi-tasks optimization fashion to interpretively formulate the Person-Job Fit process; 2) leverages deep interactive representation learning to automatically learn the interdependence between a resume and job requirements without relying on a clear list of job seeker's abilities, and deploys the optimizing problem as a learning to rank problem. Experiments on large real dataset show that the proposed IPJF model outperforms state-of-the-art baselines and also gives promising interpretable recommending reasons.

The capability of extracting sequential patterns from the user-item interaction data is now becoming a key feature of recommender systems. Though it is important to capture the sequential effect, existing methods only focus on modelling the sparse item-wise sequential effect in user preference and only consider the homogeneous user interaction behaviors (i.e., a single type of user behavior). As a result, the data sparsity issue inevitably arises and makes the learned sequential patterns fragile and unreliable, impeding the sequential recommendation performance of existing methods. Hence, in this paper, we propose AIR, namely attentional intention-aware recommender systems to predict category-wise future user intention and collectively exploit the rich heterogeneous user interaction behaviors (i.e., multiple types of user behaviors). In AIR, we propose to represent user intention as an action-category tuple to discover category-wise sequential patterns and to capture varied effect of different types of actions for recommendation. A novel attentional recurrent neural network (ARNN) is proposed to model the intention migration effect and infer users' future intention. Besides, an intention-aware factorization machine (ITFM) is developed to perform intention-aware sequential recommendation. Experiments on two real-life datasets demonstrate the superiority and practicality of AIR in sequential top-k recommendation tasks.

Online recruitment services are now rapidly changing the landscape of hiring traditions on the job market. There are hundreds of millions of registered users with resumes, and tens of millions of job postings available on the Web. Learning good job-resume matching for recruitment services is important. Existing studies on job-resume matching generally focus on learning good representations of job descriptions and resume texts with comprehensive matching structures. We assume that it would bring benefits to learn the preference of both recruiters and job-seekers from previous interview histories and expect such preference is helpful to improve job-resume matching. To this end, in this paper, we propose a novel matching network with preference modeled. The key idea is to explore the latent preference given the history of all interviewed candidates for a job posting and the history of all job applications for a particular talent. To be more specific, we propose a profiling memory module to learn the latent preference representation by interacting with both the job and resume sides. We then incorporate the preference into the matching framework as an end-to-end learnable neural network. Based on the real-world data from an online recruitment platform namely "Boss Zhipin", the experimental results show that the proposed model could improve the job-resume matching performance against a series of state-of-the-art methods. In this way, we demonstrate that recruiters and talents indeed have preference and such preference can improve job-resume matching on the job market.

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural idea of an ensemble model of the two, existing ensemble methods only focused on leveraging one approach to enhance another, we argue however that they can be further mutually enhanced with a proper training strategy. In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario. It consists of a language-model-like generator, a ranker generator, and one ranker discriminator. Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts. The experimental results on a large short-text conversation data demonstrate the effectiveness of the ensembleGAN by the amelioration on both human and automatic evaluation metrics.

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

We consider context-response matching with multiple types of representations for multi-turn response selection in retrieval-based chatbots. The representations encode semantics of contexts and responses on words, n-grams, and sub-sequences of utterances, and capture both short-term and long-term dependencies among words. With such a number of representations in hand, we study how to fuse them in a deep neural architecture for matching and how each of them contributes to matching. To this end, we propose a multi-representation fusion network where the representations can be fused into matching at an early stage, at an intermediate stage, or at the last stage. We empirically compare different representations and fusing strategies on two benchmark data sets. Evaluation results indicate that late fusion is always better than early fusion, and by fusing the representations at the last stage, our model significantly outperforms the existing methods, and achieves new state-of-the-art performance on both data sets. Through a thorough ablation study, we demonstrate the effect of each representation to matching, which sheds light on how to select them in practical systems.

Natural Language Generation (NLG), as an important part of Natural Language Processing (NLP), has begun to take full advantage of recent advances in language models. Based on recurrent neural networks (RNNs), NLG has made ground breaking improvement and is widely applied in many tasks. RNNs typically learn a joint probability of words, and the additional information is usually fed to RNNs hidden layer using implicit vector representations. Still, there exists some problem unsolved. Standard RNN is not applicable when we need to impose hard constraints on the language generation tasks: for example, standard RNNs cannot guarantee designated word(s) to appear in a target sentence to generate. In this paper, we propose a Backward-or-Forward Generative Adversarial Nets model (BoFGAN) to address this problem. Starting from a particular given word, a generative model at every time step generates a new preceding or subsequent word conditioned on the generated sequence so far until both sides reach an end. To train the generator, we first model it as a stochastic policy using Reinforcement Learning; then we employ a discriminator to evaluate the quality of a complete sequence as the end reward; and lastly, we apply Monte Carlo (MC) search to estimate the long-term return and update the generator via policy gradient. Experimental results demonstrate the effectiveness and rationality of our proposed BoFGAN model.

To have automatic conversations between human and computer is regarded as one of the most hardcore problems in computer science. Conversational systems are of growing importance due to their promising potentials and commercial values as virtual assistants and chatbots. To build such systems with adequate intelligence is challenging, and requires abundant resources including an acquisition of big conversational data and interdisciplinary techniques, such as content analysis, text mining, and retrieval. The arrival of big data era reveals the feasibility to create a conversational system empowered by data-driven approaches. Now we are able to collect an extremely large number of human-human conversations on Web, and organize them to launch human-computer conversational systems. Given a human issued utterance, i.e., a query, a conversational system will search for appropriate responses, conduct relevance ranking using contexts information, and then output the highly relevant result. In this paper, we propose a novel context modeling framework with end-to-end neural networks for human-computer conversational systems. The proposed model is general and unified. In the experiments, we demonstrate the effectiveness of the proposed model for human-computer conversations using [email protected], MAP, nDCG, and MRR metrics.

Conversation systems are of growing importance since they enable an easy interaction interface between humans and computers: using natural languages. To build a conversation system with adequate intelligence is challenging, and requires abundant resources including an acquisition of big data and interdisciplinary techniques, such as information retrieval and natural language processing. Along with the prosperity of Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will come up with at least some results from the immense repository for any user inputs. Given a human issued message, i.e., query, a traditional conversation system would provide a response after adequate training and learning of how to respond. In this paper, we propose a new task for conversation systems: joint learning of response ranking featured with next utterance suggestion. We assume that the new conversation mode is more proactive and keeps user engaging. We examine the assumption in experiments. Besides, to address the joint learning task, we propose a novel Dual-LSTM Chain Model to couple response ranking and next utterance suggestion simultaneously. From the experimental results, we demonstrate the usefulness of the proposed task and the effectiveness of the proposed model.

To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques in information retrieval, natural language processing, and data management, etc. The challenges lie in how to respond like a human, and to maintain a relevant, meaningful, and continuous conversation. The arrival of big data era reveals the feasibility to create such a system empowered by data-driven approaches. We can now organize the conversational data as a chat companion. In this paper, we introduce a chat companion system, which is a practical conversation system between human and computer as a real application. Given the human utterances as queries, our proposed system will respond with corresponding replies retrieved and highly ranked from a massive conversational data repository. Note that 'practical' here indicates effectiveness and efficiency: both issues are important for a real-time system based on a massive data repository. We have two scenarios of single-turn and multi-turn conversations. In our system, we have a base ranking without conversational context information (for single-turn) and a context-aware ranking (for multi-turn). Both rankings can be conducted either by a shallow learning or deep learning paradigm. We combine these two rankings together in optimization. In the experimental setups, we investigate the performance between effectiveness and efficiency for the proposed methods, and we also compare against a series of baselines to demonstrate the advantage of the proposed framework in terms of [email protected], MAP, and nDCG. We present a new angle to launch a practical online conversation system between human and computer.

Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. Different random feature functions have since been proposed to approximate a variety of kernel functions. Among them the Random Binning (RB) feature, proposed in the first random-feature paper [21], has drawn much less attention than the Random Fourier (RF) feature proposed also in [21]. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing R random grids with at least κ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of O(1/κ R)), which not only sharpens its O(1/√R) rate from Monte Carlo analysis, but also shows a κ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to κ. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods.

To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Along with the prosperity of Web 2.0, we are now able to collect extremely massive conversational data, which are publicly available. It casts a great opportunity to launch automatic conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will be able to find at least some responses from the massive repository for any user inputs. Given a human issued message, i.e., query, our system would provide a reply after adequate training and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data. The proposed model is general and unified for different conversation scenarios in open domain. We incorporate the impact of multiple data inputs, and formulate various features and factors with optimization into the deep learning framework. In the experiments, we investigate the effectiveness of the proposed deep neural network structures with better combinations of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of [email protected], MAP, nDCG, and MRR for conversational purposes.

In recent years, online social networks are among the most popular websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence generate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity. Compared with the extremely large data over the whole collection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenarios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effective learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth documents based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our approach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP measurements.

Video popularity prediction plays a foundational role in many aspects of life, such as recommendation systems and investment consulting. Because of its technological and economic importance, this problem has been extensively studied for years. However, four constraints have limited most related works' usability. First, most feature oriented models are inadequate in the social media environment, because many videos are published with no specific content features, such as a strong cast or a famous script. Second, many studies assume that there is a linear correlation existing between view counts from early and later days, but this is not the case in every scenario. Third, numerous works just take view counts into consideration, but discount associated sentiments. Nevertheless, it is the public opinions that directly drive a video's final success/failure. Also, many related approaches rely on a network topology, but such topologies are unavailable in many situations. Here, we propose a Dual Sentimental Hawkes Process (DSHP) to cope with all the problems above. DSHP's innovations are reflected in three ways: (1) it breaks the "Linear Correlation" assumption, and implements Hawkes Process; (2) it reveals deeper factors that affect a video's popularity; and (3) it is topology free. We evaluate DSHP on four types of videos: Movies, TV Episodes, Music Videos, and Online News, and compare its performance against 6 widely used models, including Translation Model, Multiple Linear Regression, KNN Regression, ARMA, Reinforced Poisson Process, and Univariate Hawkes Process. Our model outperforms all of the others, which indicates a promising application prospect.

This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.

Searching for a particular person by specifying her name is one of the essential functions in online social networking services such as Facebook. So many times, however, one would like to find a person but what she knows is few social labels about the target, such as interests, skills, hometown, school, employment, etc. Assume each user is associated a set of social labels, we propose a novel search in online social network, Person-of-Interest (POI) Search, which aims to find a list of desired targets based on a set of user-specified query labels that depict the targets. We develop a greedy heuristic graph search algorithm, which finds the target who not only covers the query labels, but also either possesses better social interactions with peers or has higher social proximity towards the user. Experiments conducted on Facebook and Twitter datasets exhibit the satisfying accuracy and encourage more advanced efforts on POI search.

Online social networks nowadays enjoy their worldwide prosperity, as they have revolutionized the way for people to discover, to share, and to distribute information. With millions of registered users and the proliferation of user-generated contents, the social networks become "giants", likely eligible to carry on any research tasks. However, the giants do have their Achilles Heel: extreme data sparsity. Compared with the massive data over the whole collection, individual posting documents, (e.g., a microblog less than 140 characters), seem to be too sparse to make a difference under various research scenarios, while actually they are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. We formulate a socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users. An efficient algorithm is designed to learn the proposed factor graph model. Finally we propagate term counts to smooth documents based on the estimated influence. Experimental results on Twitter and Weibo datasets validate the effectiveness of the proposed model. By leveraging the smoothed language model with social factors, our approach obtains significant improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP results.

The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://www.csie.ntu.edu.tw/~d97944007/aggregative/

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.

In many cases, people would like to read the news with great importance on the Internet. However, what users can grasp covers a very small part compared with the huge amount of news which never stops increasing. In this paper, we try to find what users are most likely to be interested in. We notice that media focus plays an essential role in distinguishing news topics and user attention is also an important factor. Therefore, we first propose five strategies which only exploit media focus to decide news influence impact. Then we provide three strategies to combine user attention with media focus. Meanwhile, we also take four types of interaction between user attention and media focus into consideration. To the best of our knowledge, this is the first work to establish different models for computing influence decay of news topics. Experiments show that better influence scores will be achieved by a decay algorithm based on Ebbinghaus forgetting curve and information fusion by considering interactions between user attention and media focus.

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators.

A large proportion of search engine queries contain phrases,namely a sequence of adjacent words. In this paper, we propose to use flat position index (a.k.a schema-independent index) for phrase query evaluation. In the flat position index, the entire document collection is viewed as a huge sequence of tokens. Each token is represented by one flat position, which is a unique position offset from the beginning of the collection. Each indexed term is associated with a list of the flat positions about that term in the sequence. To recover DocID from flat positions efficiently, we propose a novel cache sensitive look-up table (CSLT), which is much faster than existing search algorithms. Experiments on TREC GOV2 data collection show that flat position index can reduce the index size and speed up phrase querying substantially, compared with traditional word-level index.

In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists' response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the future. We have implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square). Experimental results on a real-large data set show that the best predictive model achieves a mean average predictive performance of 0.740 measured in R-square, which significantly outperforms several alternative algorithms.

Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.