Timely nowcasting is critical for public safety during fast-evolving storms, where even short delays can trigger cascading failures—as in the October 2024 Spain flash flood that claimed over 90 lives within minutes. While radar offers reliable real-time sensing of atmospheric structure, models that collapse 3D volumes into 2D slices inevitably discard vertical information essential for capturing storm growth, phase transitions, and collapse. We introduce StormMind, a physically grounded framework that forecasts convective evolution by modeling causal interactions across stratified atmospheric layers. StormMind addresses two fundamental challenges:(1) the nonlinear, asynchronous coupling between low-, mid-, and high-level processes; and (2) reflectivity uncertainty, where storms with distinct vertical structures may appear deceptively similar on radar, masking their true phase and intensity. To tackle these issues, StormMind designs: i) a Convection Dynamics Extractor that models storm evolution from two complementary perspectives—horizontal morphology, capturing the spatial organization of physical processes within individual atmospheric layers, and vertical coupling, modeling energy exchanges across layers; and ii) a Convection Manifestation Reconstructor that adaptively fuses intra- and inter-layer signals, conditioned on the evolving storm state, to infer phase transitions (e.g., initiation, intensification, dissipation). Evaluated on the large-scale 3D-NEXRAD dataset (2020–2022, U.S.), StormMind outperforms strong baselines, achieving a 14.71% gain in CSI40. In real-world deployment with the Guangzhou Meteorological Bureau (Mar–May 2025), it improves CSI40 by 9.39% and boosts early-warning accuracy (98.33%)

Logo embedding models convert the product logos in images into vectors, enabling their utilization for logo recognition and detection within e-commerce platforms. This facilitates the enforcement of intellectual property rights and enhances product search capabilities. However, current methods treat logo embedding as a purely visual problem. A noteworthy issue is that visual models capture features more than logos. Instead, we view this as a multimodal task, using text as auxiliary information to facilitate the visual model's understanding of the logo. The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding. Inspired by this, we propose an approach, FashionLOGO, to explore how to prompt MLLMs to generate appropriate text for product images, which can help visual models achieve better logo embeddings. We adopt a cross-attention transformer block that enables visual embedding to automatically learn supplementary knowledge from textual embedding. Our extensive experiments on real-world datasets prove that FashionLOGO is capable of generating generic and robust logo embeddings, achieving state-of-the-art performance in all benchmarks.

Federated learning is a privacy-focused learning paradigm, which trains a global model with gradients uploaded from multiple participants, circumventing explicit exposure of private data. However, previous research of gradient leakage attacks suggests that gradients alone are sufficient to reconstruct private data, rendering the privacy protection mechanism of federated learning unreliable. Existing defenses commonly craft transformed gradients based on ground-truth gradients to obfuscate the attacks, but often are less capable of maintaining good model performance together with satisfactory privacy protection. In this paper, we propose a novel yet effective defense framework named guarding against gradient leakage (Guardian) that produces transformed gradients by jointly optimizing two theoretically-derived metrics associated with gradients for performance maintenance and privacy protection. In this way, the transformed gradients produced via Guardian can achieve minimal privacy leakage in theory with the given performance maintenance level. Moreover, we design an ingenious initialization strategy for faster generation of transformed gradients to enhance the practicality of Guardian in real-world applications, while demonstrating theoretical convergence of Guardian to the performance of the global model. Extensive experiments on various tasks show that, without sacrificing much accuracy, Guardian can effectively defend state-of-the-art gradient leakage attacks, compared with the slight effects of baseline defense approaches.

Session-based recommendation (SBR) is a challenging task, which aims to predict users’ future interests based on anonymous behavior sequences. Existing methods leverage powerful representation learning approaches to encode sessions into a low-dimensional space. However, despite such achievements, the existing studies focus on the instance-level session learning, while neglecting the group-level users’ preferences (e.g., the common preferences of group users in repeat consumption). To this end, we propose a novelRepeat-awareNeuralMechanism forSession-basedRecommendation (RNMSR). In RNMSR, we propose to learn the user preference from two levels: (i)instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users’ preference in instance-level. (ii)group-level, which converts sessions into group-level behavior patterns to model the group-level users’ preferences. In RNMSR, we combine instance-level and group-level user preference to model the repeat consumption of users, i.e., whether users take repeated consumption and which items are preferred by users. Extensive experiments are conducted on three real-world datasets, i.e., Diginetica, Yoochoose, and Nowplaying, demonstrating that the proposed method consistently achieves state-of-the-art performance in all the tests.

Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, an end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks and different PLMs show that CP-Tuning outperforms state-of-the-art methods.

The successful application of Pre-Trained Models (PTMs) has revolutionized the development of Natural Language Processing (NLP) by large-scale self-supervised pre-training. However, it is not easy to obtain high-performing models in domain-specific applications and deploy them online with strict QPS (Query Per Second) requirements for industrial practitioners. To solve these issues, the EasyNLP toolkit is designed for building PTM-based NLP applications with ease, which supports a comprehensive suite of NLP algorithms and is suitable for meeting the inference requirements in industry. It features knowledge-enhanced pre-training that captures rich domain knowledge to better support domain-specific applications. In addition, the knowledge distillation and prompt-based few-shot learning functionalities are provided to improve the performance of large-scale PTMs with little training data available, and to distill models to smaller ones that are suitable for online deployment. EasyNLP provides a unified framework of model training, inference and deployment for real-world applications, using simple high-level APIs or command-line tools. Currently, EasyNLP has powered over ten business units within Alibaba Group and is seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud. EasyNLP is also beneficial for academia, as it integrates state-of-the-art methods and models to make it easy for researchers to benchmark and develop their own algorithms. We have released EasyNLP to public at GitHub (https://github.com/alibaba/EasyNLP).

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (k NN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.

Information-seeking conversation systems are increasingly popular in real-world applications, especially for e-commerce companies. To retrieve appropriate responses for users, it is necessary to compute the matching degrees between candidate responses and users' queries with historical dialogue utterances. As the contexts are usually much longer than responses, it is thus necessary to expand the responses (usually short) with richer information. Recent studies on pseudo-relevance feedback (PRF) have demonstrated its effectiveness in query expansion for search engines, hence we consider expanding response using PRF information. However, existing PRF approaches are either based on heuristic rules or require heavy manual labeling, which are not suitable for solving our task. To alleviate this problem, we treat the PRF selection for response expansion as a learning task and propose a reinforced learning method that can be trained in an end-to-end manner without any human annotations. More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as a reward to guide the selector to extract useful PRF terms, which boosts the overall task performance. Extensive experiments on both standard benchmarks and commercial datasets prove the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both case studies and quantitative analysis show that our model is capable of selecting meaningful PRF terms to expand response candidates and also achieving the best results compared with all baselines on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system.

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose. To bridge this gap, the EasyTransfer platform is designed to develop deep TL algorithms for NLP applications. EasyTransfer is backended with a high-performance and scalable engine for efficient training and inference, and also integrates comprehensive deep TL algorithms, to make the development of industrial-scale TL applications easier. In EasyTransfer, the built-in data and model parallelism strategies, combined with AI compiler optimization, show to be 4.0x faster than the community version of distributed training. EasyTransfer supports various NLP models in the ModelZoo, including mainstream PLMs and multi-modality models. It also features various in-house developed TL algorithms, together with the AppZoo for NLP applications. The toolkit is convenient for users to quickly start model training, evaluation, and online deployment. EasyTransfer is currently deployed at Alibaba to support a variety of business scenarios, including item recommendation, personalized search, conversational question answering, etc. Extensive experiments on real-world datasets and online applications show that EasyTransfer is suitable for online production with cutting-edge performance for various applications. The source code of EasyTransfer is released at Github1.

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous efforts mostly use homogeneous knowledge (especially structured relation triples in knowledge graphs) to enhance the context-aware representations of entity mentions, whose performance may be limited by the coverage of knowledge graphs. Also, it is unclear whether these KEPLMs truly understand the injected semantic knowledge due to the "black-box'' training mechanism. In this paper, we propose a novel KEPLM named HORNET, which integrates Heterogeneous knowledge from various structured and unstructured sources into the Roberta NETwork and hence takes full advantage of both linguistic and factual knowledge simultaneously. Specifically, we design a hybrid attention heterogeneous graph convolution network (HaHGCN) to learn heterogeneous knowledge representations based on the structured relation triplets from knowledge graphs and the unstructured entity description texts. Meanwhile, we propose the explicit dual knowledge understanding tasks to help induce a more effective infusion of the heterogeneous knowledge, promoting our model for learning the complicated mappings from the knowledge graph embedding space to the deep context-aware embedding space and vice versa. Experiments show that our HORNET model outperforms various KEPLM baselines on knowledge-aware tasks including knowledge probing, entity typing and relation extraction. Our model also achieves substantial improvement over several GLUE benchmark datasets, compared to other KEPLMs.

User intent detection is vital for understanding their demands in dialogue systems. Although the User Intent Classification (UIC) task has been widely studied, for large-scale industrial applications, the task is still challenging. This is because user inputs in distinct domains may have different text distributions and target intent sets. When the underlying application evolves, new UIC tasks continuously emerge in a large quantity. Hence, it is crucial to develop a framework for large-scale extensible UIC that continuously fits new tasks and avoids catastrophic forgetting with an acceptable parameter growth rate. In this paper, we introduce the Meta Lifelong Learning (MeLL) framework to address this task. In MeLL, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning. We design global and local memory networks to capture the cross-task prototype representations of different classes, treated as the meta-learner quickly adapted to different tasks. Additionally, the Least Recently Used replacement policy is applied to manage the global memory such that the model size does not explode through time. Finally, each UIC task has its own task-specific output layer, with the attentive summarization of various features. We have conducted extensive experiments on both open-source and real industry datasets. Results show that MeLL improves the performance compared with strong baselines and also reduces the number of total parameters. We have also deployed MeLL on a real-world e-commerce dialogue system AliMe and observed significant improvements in terms of both F1 and the resources usage.

Question Answering (QA) systems have been extensively studied in both academia and the research community due to their wide real-world applications. When building such industrial-scale QA applications, we are facing two prominent challenges, i.e., i) lacking a sufficient amount of training data to learn an accurate model and ii) requiring high inference speed for online model serving. There are generally two ways to mitigate the above-mentioned problems. One is to adopt transfer learning to leverage information from other domains; the other is to distill the “dark knowledge” from a large teacher model to small student models. The former usually employs parameter sharing mechanisms for knowledge transfer, but does not utilize the “dark knowledge” of pre-trained large models. The latter usually does not consider the cross-domain information from other domains. We argue that these two types of methods can be complementary to each other. Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. Our framework considers the “dark knowledge” learned from large teacher models and also leverages the adaptive hints to alleviate the domain differences between teacher and student models. Extensive experiments have been conducted on two text matching tasks for retrieval-based QA systems. Results show the proposed method has better performance than the competing methods including the existing state-of-the-art transfer learning methods. We have also deployed our method in an online production system and observed significant improvements compared to the existing approaches in terms of both accuracy and cross-domain robustness.

In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry. Different from the matching in the general domain, the fashion matching is required to pay much more attention to the fine-grained information in the fashion images and texts. Pioneer approaches detect the region of interests (i.e., RoIs) from images and use the RoI embeddings as image representations. In general, RoIs tend to represent the "object-level" information in the fashion images, while fashion texts are prone to describe more detailed information, e.g. styles, attributes. RoIs are thus not fine-grained enough for fashion text and image matching. To this end, we propose FashionBERT, which leverages patches as image features. With the pre-trained BERT model as the backbone network, FashionBERT learns high level representations of texts and images. Meanwhile, we propose an adaptive loss to trade off multitask learning in the FashionBERT modeling. Two tasks (i.e., text and image matching and cross-modal retrieval) are incorporated to evaluate FashionBERT. On the public dataset, experiments demonstrate FashionBERT achieves significant improvements in performances than the baseline and state-of-the-art approaches. In practice, FashionBERT is applied in a concrete cross-modal retrieval application. We provide the detailed matching performance and inference efficiency analysis.

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model “IART”, which refers to “Intent-Aware Ranking with Transformers”. IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets [35]. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.

Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.

Product search and recommendation is a task that every e-commerce platform wants to outperform their peels on. However, training a good search or recommendation model often requires more data than what many platforms have. Fortunately, the search tasks on different platforms share the common underlying structure. Considering each platform as a domain, we propose a cross-domain learning approach to help the task on data-deficient platforms by leveraging the data from data-abundant platforms. In our solution, the importance of features in different domains is addressed by a domain-specific attention network. Meanwhile, a multi-task regularizer based on Wasserstein distance is introduced to help extract both domain-invariant and domain-specific features. Our model consistently outperforms the competing methods on both public and real-world industry datasets. Quantitative evaluation shows that our model can discover important features for different domains, which helps us better understand different user needs across platforms. Last but not least, we have deployed our model online in three big e-commerce platforms namely Taobao, Tmall, and Qintao, and observed better performance than the production models for all the platforms.

Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding.

Deep neural network based transfer learning has been widely used to leverage information from the domain with rich data to help domain with insufficient data. When the source data distribution is different from the target data, transferring knowledge between these domains may lead to negative transfer. To mitigate this problem, a typical way is to select useful source domain data for transferring. However, limited studies focus on selecting high-quality source data to help neural network based transfer learning. To bridge this gap, we propose a general Minimax Game based model for selective Transfer Learning (MGTL). More specifically, we build a selector, a discriminator and a TL module in the proposed method. The discriminator aims to maximize the differences between selected source data and target data, while the selector acts as an attacker to selected source data that are close to the target to minimize the differences. The TL module trains on the selected data and provides rewards to guide the selector. Those three modules play a minimax game to help select useful source data for transferring. Our method is also shown to speed up the training process of the learning task in the target domain than traditional TL methods. To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning. To examine the generality of our method, we evaluate it on two different tasks: item recommendation and text retrieval. Extensive experiments over both public and real-world datasets demonstrate that our model outperforms the competing methods by a large margin. Meanwhile, the quantitative evaluation shows our model can select data which are close to target data. Our model is also deployed in a real-world system and significant improvement over the baselines is observed.

Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.

Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.

Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insufficient labeled data, these approaches can be used in a Transfer Learning (TL) setting to leverage labeled data from a resource-rich source domain. To achieve better performance, source domain data selection is essential in this process to prevent the "negative transfer" problem. However, the emerging deep transfer models do not fit well with most existing data selection methods, because the data selection policy and the transfer learning model are not jointly trained, leading to sub-optimal training efficiency. In this paper, we propose a novel reinforced data selector to select high-quality source domain data to help the TL model. Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector. We build the reinforced data selector based on the actor-critic framework and integrate it to a DNN based transfer learning model, resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough experimental evaluation on two major tasks for text matching, namely, paraphrase identification and natural language inference. Experimental results show the proposed RTL can significantly improve the performance of the TL model. We further investigate different settings of states, rewards, and policy optimization methods to examine the robustness of our method. Last, we conduct a case study on the selected data and find our method is able to select source domain data whose Wasserstein distance is close to the target domain data. This is reasonable and intuitive as such source domain data can provide more transferability power to the model.

Consumers today face too many reviews to read when shopping online. Presenting the most helpful reviews, instead of all, to them will greatly ease purchase decision making. Most of the existing studies on review helpfulness prediction focused on domains with rich labels, not suitable for domains with insufficient labels. In response, we explore a multi-domain approach that learns domain relationships to help the task by transferring knowledge from data-rich domains to data-deficient domains. To better model domain differences, our approach gates multi-granularity embeddings in a Neural Network (NN) based transfer learning framework to reflect the domain-variant importance of words. Extensive experiments empirically demonstrate that our model outperforms the state-of-the-art baselines and NN-based methods without gating on this task. Our approach facilitates more effective knowledge transfer between domains, especially when the target domain dataset is small. Meanwhile, the domain relationship and domain-specific embedding gating are insightful and interpretable.

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intent on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.

Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain ''chit-chat'' conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.

Recent benchmark studies show that MPI-based distributed implementations of DBSCAN, e.g., PDSDBSCAN, outperform other implementations such as apache Spark etc. However, the communication cost of MPI DBSCAN increases drastically with the number of processors, which makes it inefficient for large scale problems.

We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.

Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).

Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics , and in Twitter, they are called hashtags . In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not give explicit ratings to microtopics. Second, there exists rich information about users and microtopics, for example, users' published content and biographical information, but it is not clear how to best utilize such information. To address the above two challenges, we propose a joint probabilistic latent factor model to integrate rich information into a matrix factorization-based solution to microtopic recommendation. Our model builds on top of collaborative filtering, content analysis, and feature regression. Using two real-world datasets, we evaluate our model with different kinds of content and contextual information. Experimental results show that our model significantly outperforms a few competitive baseline methods, especially in the circumstance where users have few adoption behaviors.

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users/sources. FaitCrowd jointly models the process of generating question content and sources' provided answers in a probabilistic model to estimate both topical expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, FaitCrowd demonstrates better ability to obtain true answers for the questions compared with existing approaches. Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, users may provide rich side information; for example, users may write arguments to back up their stances, interact with each other, and provide biographical information. In this work, we propose an integrated model to leverage side information. Our proposed method is a regression-based latent factor model which jointly models user arguments, interactions, and attributes. Our method can perform stance prediction for both warm-start and cold-start users. We demonstrate in experiments that our method has promising results on both micro-level and macro-level stance prediction.

Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings.

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods.

Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.

Textual information exchanged among users on online social network platforms provides deep understanding into users’ interest and behavioral patterns. However, unlike traditional text-dominant settings such as offline publishing, one distinct feature for online social network is users’ rich interactions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich. We conduct experiments on real Twitter data to demonstrate that the topics obtained by our model are both informative and insightful. As an application of our B-LDA model, we also propose a Twitter followee recommendation algorithm combining B-LDA and LDA, which we show in a quantitative experiment outperforms LDA with a significant margin.