As Large Language Models (LLMs) are increasingly popularized in the multilingual world, ensuring hallucination-free factuality becomes markedly crucial. However, existing benchmarks for evaluating the reliability of Multimodal Large Language Models (MLLMs) predominantly focus on textual or visual modalities with a primary emphasis on English, which creates a gap in evaluation when processing multilingual input, especially in speech. To bridge this gap, we propose a novel Cross-lingual and Cross-modal Factuality benchmark (CCFQA). Specifically, the CCFQA benchmark contains parallel speech-text factual questions across 8 languages, designed to systematically evaluate MLLMs' cross-lingual and cross-modal factuality capabilities. Our experimental results demonstrate that current MLLMs still face substantial challenges on the CCFQA benchmark. Furthermore, we propose a few-shot transfer learning strategy that effectively transfers the Question Answering (QA) capabilities of LLMs in English to multilingual Spoken Question Answering (SQA) tasks, achieving competitive performance with GPT-4o-mini-Audio using just 5-shot training. We release CCFQA as a foundational research resource to promote the development of MLLMs with more robust and reliable speech understanding capabilities.

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet they generally lack self-awareness, often displaying overconfidence when confronted with questions beyond their knowledge boundaries. This limitation severely hinders their trustworthiness in high-stakes scenarios. Existing calibration methods typically rely on sampling accuracy, derived from multiple outputs, as a proxy for model confidence. However, this coarse-grained metric fails to capture the model’s internal cognitive states, such as confusion, hallucination, or persistent belief in false knowledge. To address this, we propose CogConf (Cognitive Confidence), a cognitively grounded uncertainty signal that extends sampling accuracy by incorporating the semantic diversity of incorrect answers and the model’s abstention behaviors. By shifting the focus from sampling-based to cognition-oriented uncertainty modeling, CogConf offers a more faithful reflection of the model's internal beliefs. Building on this signal, we introduce CogAlign, a simple yet effective alignment framework that explicitly aligns the model’s verbalized confidence with CogConf, thereby producing uncertainty estimates that better reflect the model’s internal cognition. Experimental results on six knowledge-intensive in-domain and out-of-domain QA datasets demonstrate that CogConf robustly characterizes the model's internal uncertainty. Building on this foundation, CogAlign guides the model's expression to significantly enhance the trustworthiness and utility of its uncertainty calibration without compromising its underlying QA capabilities, while also demonstrating strong cross-task generalization and output stability. Offering a new pathway toward building more trustworthy LLMs.

Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Rongchuan Mu, Zexin Wang, Qianyu Wang, MingHua Ma, Zekun Wang, Ming Liu, Bing Qin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Wenxiang Zheng, Guo Tang, Shixin Jiang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Ming Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Generative Recommenders (GRs), exemplified by the Hierarchical Sequential Transduction Unit (HSTU), have emerged as a powerful paradigm for modeling long user interaction sequences. However, we observe that their ''flat-sequence'' assumption overlooks the rich, intrinsic structure of user behavior. This leads to two key limitations: a failure to capture the temporal hierarchy of session-based engagement, and computational inefficiency, as dense attention introduces significant noise that obscures true preference signals within semantically sparse histories, which deteriorates the quality of the learned representations. To this end, we propose a novel framework named HPGR (Hierarchical and Preference-aware Generative Recommender), built upon a two-stage paradigm that injects these crucial structural priors into the model to handle the drawback. Specifically, HPGR comprises two synergistic stages. First, a structure-aware pre-training stage employs a session-based Masked Item Modeling (MIM) objective to learn a hierarchically-informed and semantically rich item representation space. Second, a preference-aware fine-tuning stage leverages these powerful representations to implement a Preference-Guided Sparse Attention mechanism, which dynamically constrains computation to only the most relevant historical items, enhancing both efficiency and signal-to-noise ratio. Empirical experiments on a large-scale proprietary industrial dataset from APPGallery and an online A/B test verify that HPGR achieves state-of-the-art performance over multiple strong baselines, including HSTU and MTGR.

Video question answering (VideoQA) necessitates simultaneous understanding of visual and linguistic information, requiring both in-depth analysis of individual modality features and the establishment of cross-modal correlations to achieve precise reasoning. However, VideoQA models often struggle with irrelevant temporal and spatial noise due to the dense events and concepts in real-world complex video contents. Previous works reduce noise by only sampling a fixed number of visual tokens at the patch level, overlooking the variation in the required granularities of features and quantities of visual cues across different question conditions. To address these, we propose an Aggregating-then-Pruning Sampler (APSam), which diversifies feature granularities and adaptively denoises on a per-question basis. Specifically, we propose a conditional token aggregator to obtain multi-granularity visual semantics by merging similar question-relevant tokens. Then, we propose a conditional token pruner, which restricts noise tokens through a variable-capacity receptive field determined by the inputs. Experimental results show that APSam achieves significant performance on three challenging complex VideoQA datasets,i.e., AGQAv2, NExT-QA, and STAR. Further analyses reveal that the APSam also exhibits high reasoning capability and interpretability.

Multi-hop Knowledge Graph Reasoning (KGR) seeks to identify accurate answers within Knowledge Graphs (KGs) via multi-step reasoning, predominantly utilizing reinforcement learning (RL) to enhance the efficiency of the reasoning process. Unlike traditional Knowledge Graph Embedding (KGE) methods, RL-based approaches offer superior interpretability. However, these methods often underperform due to two critical limitations: (1) their over-reliance on Horn rules for reasoning paths, which restricts their expressive power; and (2) inadequate utilization of reasoning states during the process. To address these issues, we propose a novel RL-based framework, RAR, which shifts focus from individual paths to subgraph structures for more robust predictions. RAR frames the retrieval of reasoning subgraphs from the KG as a Markov Decision Process (MDP) and incorporates a subgraph retriever. To efficiently explore the extensive subgraph space, we integrate multi-agent RL to enhance the retriever's capabilities. Additionally, RAR features an advanced analyst module that meticulously examines reasoning states. These modules function iteratively: the retriever expands the subgraph, followed by the analyst module's in-depth analysis. The insights gained are then used to inform subsequent retrieval steps. Ultimately, the predicted scores from both modules are synthesized to produce more precise posterior scores. Experimental results across multiple datasets demonstrate RAR's efficacy, showcasing a notable improvement over existing state-of-the-art RL-based KGR methods.

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we highlight the potential for automatically discovering policies directly from raw, real-world dialogue records. To this end, we introduce a novel dialogue policy planning framework, LDPP. It fully automates the process from mining policies in dialogue records to learning policy planning. Specifically, we employ a variant of the Variational Autoencoder to discover fine-grained policies represented as latent vectors. After automatically annotating the data with these latent policy labels, we propose an Offline Hierarchical Reinforcement Learning (RL) algorithm in the latent space to develop effective policy planning capabilities. Our experiments demonstrate that LDPP outperforms existing methods on two proactive scenarios, even surpassing ChatGPT with only a 1.8-billion-parameter LLM.

Zekun Wang, MingHua Ma, Zexin Wang, Rongchuan Mu, Liping Shan, Ming Liu, Bing Qin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Guo Tang, Zheng Chu, Wenxiang Zheng, Junjia Xiang, Yizhuo Li, Weihao Zhang, Ming Liu, Bing Qin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structured sparsity by removing redundant parameters, has recently been explored for LLM acceleration. Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. In contrast, structured pruning can reduce latency on general devices. However, it remains a challenge to perform structured pruning efficiently and maintain performance, especially at high sparsity ratios. To this end, we introduce an efficient structured pruning framework named CFSP, which leverages both Coarse (interblock) and Fine-grained (intrablock) activation information as an importance criterion to guide pruning. The pruning is highly efficient, as it only requires one forward pass to compute feature activations. Specifically, we first allocate the sparsity budget across blocks based on their importance and then retain important weights within each block. In addition, we introduce a recovery fine-tuning strategy that adaptively allocates training overhead based on coarse-grained importance to further improve performance. Experimental results demonstrate that CFSP outperforms existing methods on diverse models across various sparsity budgets. Our code will be available at https://github.com/wyxscir/CFSP.

Despite the remarkable reasoning capabilities demonstrated by large language models (LLM), the substantial computational overhead limits their practices. Some efforts have been directed toward distilling multi-step reasoning capabilities into smaller models through chain-of-thought (CoT). While CoT facilitates multi-step reasoning, the dependencies between reasoning steps are not always clearly discernible, which may lead to inconsistent reasoning. In this paper, we introduce fine-grained attribution reasoning distillation (FARD), which incorporates grounded citations to consolidate the relationships between reasoning steps. Specifically, FARD distills attribution reasoning rationales from LLMs to substitute CoT reasonings, which clarifies the dependencies among reasoning steps. Besides, we regularize the model’s attention pattern by leveraging the causal dependencies between reasoning steps, thereby enhancing the consistency of reasoning. Grounded attribution reasoning also enhances interpretability and verifiability, thereby facilitating faithful reasoning. We evaluate FARD on mathematical and general reasoning benchmarks. The experimental results indicate that FARD outperforms CoT distillation methods in mathematical reasoning, demonstrating its effectiveness. Furthermore, the small models trained with FARD have shown outstanding performance in out-of-distribution reasoning, proving strong generalization capabilities.

Evaluating the graph comprehension and reasoning abilities of Large Language Models (LLMs) is challenging and often incomplete. Existing benchmarks focus primarily on pure graph understanding, lacking a comprehensive evaluation across all graph types and detailed capability definitions. This paper presents GraCoRe, a benchmark for systematically assessing LLMs’ graph comprehension and reasoning. GraCoRe uses a three-tier hierarchical taxonomy to categorize and test models on pure graph and heterogeneous graphs, subdividing capabilities into 10 distinct areas tested through 19 tasks. Our benchmark includes 11 datasets with 5,140 graphs of varying complexity. We evaluate four closed-source and eight open-source LLMs, conducting thorough analyses from both ability and task perspectives. Key findings reveal that OpenAI o1 model has amazing comprehension and reasoning capabilities, semantic enrichment enhances reasoning performance, node ordering impacts task success, and the ability to process longer texts does not necessarily improve graph comprehension or reasoning.

Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs.Traditional methods often face challenges in semantic understanding and scalability.The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies.This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment.We further summarize key benchmarks, evaluation metrics, and discuss future directions.This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment.* These authors contributed equally.

Graph-theoretic problems arise in real-world applications like logistics, communication networks, and traffic optimization.These problems are often complex, noisy, and irregular, posing challenges for traditional algorithms.Large language models offer potential solutions but face several challenges, including limited accuracy, input length constraints, and suboptimal algorithm selection.To address these challenges, we propose MA-GTS (Multi-Agent Graph Theory Solver), a multi-agent framework that decomposes these complex problems through agent collaboration.MA-GTS maps the implicitly expressed textbased graph data into clear, structured graph representations and dynamically selects the most suitable algorithm based on problem constraints and graph structure scale.We validate MA-GTS using the G-REAL dataset, a real-world-inspired graph theory dataset we created.Experimental results show that MA-GTS outperforms state-of-the-art methods in cost-effectiveness, accuracy, and scalability, achieving strong results on multiple benchmarks (G-REAL 93.6%, GraCoRe 96.9% NL-Graph 98.4%) with robust performance on both closed-and open-source base models.

Diffusion Probabilistic Models (DPMs) have achieved significant success in generative tasks. However, their training and sampling processes suffer from the issue of distribution mismatch. During the denoising process, the input data distributions differ between the training and inference stages, potentially leading to inaccurate data generation. To obviate this, we analyze the training objective of DPMs and theoretically demonstrate that this mismatch can be alleviated through Distributionally Robust Optimization (DRO), which is equivalent to performing robustness-driven Adversarial Training (AT) on DPMs. Furthermore, for the recently proposed Consistency Model (CM), which distills the inference process of the DPM, we prove that its training objective also encounters the mismatch issue. Fortunately, this issue can be mitigated by AT as well. Based on these insights, we propose to conduct efficient AT on both DPM and CM. Finally, extensive empirical studies validate the effectiveness of AT in diffusion-based models. The code is available at https://github.com/kugwzk/AT_Diff.

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency.However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals.To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors.Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for user-tailored dialogue policies.Building on these insights, we propose the User-Tailored Dialogue Policy Planning (UDP) framework, which integrates an Intrinsic User World Model to capture user traits and feedback.UDP operates in three stages: (1) User Persona Portraying, employing a diffusion model to dynamically infer user profiles; (2) User Feedback Anticipating, using a Brownian Bridge-inspired mechanism to predict user reactions; and (3) User-Tailored Policy Planning, synthesizing these elements to optimize response strategies.To enhance robustness, we introduce an active learning approach that prioritizes challenging user personas during training.Extensive experiments across benchmarks, including both collaborative and non-collaborative settings, demonstrate UDP's effectiveness in learning user-specific dialogue strategies.Results confirm the framework's utility, highlighting its robustness, adaptability, and potential to advance user-centric dialogue systems.

Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Haotian Wang, Ming Liu, Bing Qin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature.Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance.Inspired by the dualprocess theory in psychology, which identifies two distinct modes of thinking-intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework.DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar contexts and a deliberative Monte Carlo Tree Search (MCTS) mechanism for complex, novel scenarios.This dual strategy is further coupled with a novel two-stage training regimen: offline Reinforcement Learning for robust initial policy model formation followed by MCTS-enhanced on-thefly learning, which ensures a dynamic balance between efficiency and strategic depth.Our empirical evaluations across diverse dialogue tasks affirm DPDP's superiority in achieving both high-quality dialogues and operational efficiency, outpacing existing methods. 1

Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications. Moreover, the degree of redundancy in token representations and model parameters, such as attention heads, varies significantly for different inputs. In light of the challenges, we propose SmartTrim, an adaptive acceleration framework for VLMs, which adjusts the computational overhead per instance. Specifically, we integrate lightweight modules into the original backbone to identify and prune redundant token representations and attention heads within each layer. Furthermore, we devise a self-distillation strategy to enhance the consistency between the predictions of the pruned model and its fully-capacity counterpart. Experimental results across various vision-language tasks consistently demonstrate that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation, highlighting the effectiveness and efficiency compared to previous approaches. Code will be available at https://github.com/kugwzk/SmartTrim.

Multi-modal Named Entity Recognition, a fundamental task for multi-modal knowledge graph construction, requires integrating multi-modal information to extract named entities from text. Previous research has explored the integration of multi-modal representations at different granularities. However, they struggle to integrate all these multi-modal representations to provide rich contextual information to improve multi-modal named entity recognition. In this paper, we propose DPE-MNER, which is an iterative reasoning framework that dynamically incorporates all the diverse multi-modal representations following the strategy of “decompose, prioritize, and eliminate”. Within the framework, the fusion of diverse multi-modal representations is decomposed into hierarchically connected fusion layers that are easier to handle. The incorporation of multi-modal information prioritizes transitioning from “easy-to-hard” and “coarse-to-fine”. The explicit modeling of cross-modal relevance eliminate the irrelevances that will mislead the MNER prediction. Extensive experiments on two public datasets have demonstrated the effectiveness of our approach.

Multi-modal relation extraction (MRE) requires the integration of multi-modal information to identify relationships between entities. Although fine-grained correlations between visual objects and textual words have the potential to improve cross-modal interaction, they are typically modeled implicitly and hindered by the modality gap. This paper introduces a novel method called relational Graph-Bridged cross-modal InTeraction (GBIT). GBIT aims to model fine-grained cross-modal correlations into the interaction process explicitly. This is achieved by constructing a fine-grained cross-modal relational graph, which acts as a bridge for effective cross-modal interaction in multiple layers. Within GBIT, a gated interaction strategy and an adaptive integration module are proposed for irrelevance-filtered information exchange and final information collation. Through extensive experiments on the benchmark MRE, we demonstrate the superiority of our proposed method for MRE.

Despite recent progress made by large language models in code generation, they still struggle with programs that meet complex requirements. Recent work utilizes plan-and-solve decomposition to decrease the complexity and leverage self-tests to refine the generated program. Yet, planning deep-inside requirements in advance can be challenging, and the tests need to be accurate to accomplish self-improvement. To this end, we propose FunCoder, a code generation framework incorporating the divide-and-conquer strategy with functional consensus. Specifically, FunCoder recursively branches off sub-functions as smaller goals during code generation, represented by a tree hierarchy. These sub-functions are then composited to attain more complex objectives. Additionally, we designate functions via a consensus formed by identifying similarities in program behavior, mitigating error propagation. FunCoder outperforms state-of-the-art methods by +9.8% on average in HumanEval, MBPP, xCodeEval and MATH with GPT-3.5 and GPT-4. Moreover, our method demonstrates superiority on smaller models: With FunCoder, StableCode-3b surpasses GPT-3.5 by +18.6% and achieves 97.7% of GPT-4's performance on HumanEval. Further analysis reveals that our proposed dynamic function decomposition is capable of handling complex requirements, and the functional consensus prevails over self-testing in correctness evaluation.

Dynamic scene graph generation aims to identify visual relationships (subject-predicate-object) in frames based on spatio-temporal contextual information in the video. Previous work implicitly models the spatio-temporal interaction simultaneously, which leads to entanglement of spatio-temporal contextual information. To this end, we propose a Grafting-Then-Reassembling framework (GTR), which explicitly extracts intra-frame spatial information and inter-frame temporal information in two separate stages to decouple spatio-temporal contextual information. Specifically, we first graft a static scene graph generation model to generate static visual relationships within frames. Then we propose the temporal dependency model to extract the temporal dependencies across frames, and explicitly reassemble static visual relationships into dynamic scene graphs. Experimental results show that GTR achieves the state-of-the-art performance on Action Genome dataset. Further analyses reveal that the reassembling stage is crucial to the success of our framework.

Multimodal emotion recognition in conversations (ERC) aims to identify the emotional state of constituent utterances expressed by multiple speakers in dialogue from multimodal data. Existing multimodal ERC approaches focus on modeling the global context of the dialogue and neglect to mine the characteristic information from the corresponding utterances expressed by the same speaker. Additionally, information from different modalities exhibits commonality and diversity for emotional expression. The commonality and diversity of multimodal information are compensated for each other but not effectively exploited in previous multimodal ERC works. To tackle these issues, we propose a novel Multimodal Adversarial Learning Network (MALN). MALN first mines the speaker’s characteristics from context sequences and then incorporate them with the unimodal features. Afterward, we design a novel adversarial module AMDM to exploit both commonality and diversity from the unimodal features. Finally, AMDM fuses different modalities to generate refined utterance representations for emotion classification. Extensive experiments are conducted on two public multimodal ERC datasets, IEMOCAP and MELD. Through the experiments, MALN shows its superiority over the state-of-the-art methods.

On vision-language understanding (VLU) tasks, fusion-encoder vision-language models achieve superior results but sacrifice efficiency because of the simultaneous encoding of images and text.On the contrary, the dual-encoder model that separately encodes images and text has the advantage in efficiency, while failing on VLU tasks due to the lack of deep cross-modal interactions.To get the best of both worlds, we propose DIDE 1 , a framework that distills the knowledge of the fusion-encoder teacher model into the dual-encoder student model.Since the cross-modal interaction is the key to the superior performance of teacher model but is absent in the student model, we encourage the student not only to mimic the predictions of teacher, but also to calculate the cross-modal attention distributions and align with the teacher.Experimental results demonstrate that DIDE is competitive with the fusion-encoder teacher model in performance (only a 1% drop) while enjoying 4× faster inference.Further analyses reveal that the proposed cross-modal attention distillation is crucial to the success of our framework.

Providing timely accessibility reminders (such as closed and relocated) of a point-of-interest (POI) plays a vital role in improving user satisfaction of finding places and making visiting decisions. However, it is difficult to keep the POI database in sync with the real-world counterparts due to the dynamic nature of business changes and innovations. To alleviate this problem, we formulate and present a practical solution that jointly extracts POI mentions and identifies their coupled accessibility labels from unstructured text (hereafter referred to as joint POI and accessibility extraction). We approach this task as a sequence tagging problem, where the goal is to produce (POI name, accessibility label) pairs from unstructured text. This task is challenging because of two main issues: (1) POI names are often newly-coined words so as to successfully register new entities or brands and (2) there may exist multiple pairs in the text, which necessitates dealing with one-to-many or many-to-one mapping to make each POI coupled with its matching accessibility label. To this end, we propose a Geographic-Enhanced and Dependency-guIded sequence Tagging (GEDIT) model to concurrently address the two challenges. First, to alleviate challenge #1, we develop a geographic-enhanced pre-trained model to learn the text representations, which is able to significantly relieve the problem of newly-coined words. Second, to mitigate challenge #2, we apply a relational graph convolutional network to learn the tree node representations from the parsed dependency tree, which enables us to establish a correlation between a POI and its accessibility label. Finally, we construct a neural sequence tagging model by integrating and feeding the previously pre-learned representations into a CRF layer. Extensive experiments conducted on a real-world dataset demonstrate the superiority and effectiveness of GEDIT. In addition, it has already been deployed in production at Baidu Maps, and it successfully keeps processing hundreds of thousands of Web documents every week. Statistics show that the proposed solution can save significant human effort and labor costs to deal with the same amount of documents, which confirms that it is a practical way for POI accessibility maintenance.

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC. To fully exploit such discourse structure in multiparty dialogue, we present a discourse-aware dialogue graph neural network, DADgraph, which explicitly constructs the dialogue graph using discourse dependency links and discourse relations. To validate our model, we perform experiments on the Molweni corpus, a large-scale MRC dataset built over multiparty dialogue annotated with discourse structure. Experiments on Molweni show that our discourse-aware model achieves statistically significant improvements compared against strong neural network MRC baselines.

Knowledge plays an essential role in biomedical literature search (BLS) systems, filling the semantic gap between queries and documents. Knowledge bases, constructed by human experts or machine learning methods, are generally regarded as the main sources serving external knowledge. However, a good knowledge base must balances its particularity and generalization, resulting limited knowledge coverage and utilization to BLS systems. Considering massive documents in a BLS system, and recently developing Open IE techniques by which we can automatically extract structured knowledge from documents, how about harnessing distilled internal knowledge rather than external knowledge to conduct BLS systems? Internal knowledge, providing tailored particular knowledge to BLS systems, is supposed to lead better knowledge utilization and much more competitive performance on literature search. In this paper, we design an novel internal knowledge driven BLS system upon a Multi-layered Encoders incorporating Multi-layered internal Knowledge graph, called MEMK. MEMK harnesses distilled internal structural knowledge, empowering interactive representations learning of query and documents. The experiments show that MEMK outperforms strong baselines on a public benchmark, and internal knowledge based query expansion can further improve the performance to a new state of the art.

Research into the area of multiparty dialog has grown considerably over recent years.We present the Molweni dataset 1 , a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog.Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances.We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions.Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; (Asher et al., 2016)) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing.Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F 1 on Molweni's questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.

Facing lots of name mentions appearing on the web, entity linking is essential for many information processing applications. To improve linking accuracy, the relations between entities are usually considered in the linking process. This kind of method is called collective entity linking and can obtain high-quality results. There are two kinds of information helpful to reveal the relations between entities, i.e., contextual information and structural information of entities. Most traditional collective entity linking methods consider them separately. In fact, these two kinds of information represent entities from specific and diverse views and can enhance each other, respectively. Besides, if we look into each view closely, it can be separated into sub-views that are more meaningful. For this reason, this article proposes a multi-view–based collective entity linking algorithm, which combines several views of entities into an objective function for entity linking. The importance of each view can be valued and the linking results can be obtained along with resolving this objective function. Experimental results demonstrate that our linking algorithm can acquire higher accuracy than many state-of-the-art entity linking methods. Besides, since we simplify the entity's structure and change the entity linking to a sub-matrix searching problem, our algorithm also obtains high efficiency.

We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics.Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training and evaluation resources. To address this, we develop a multi-topic aware long short-term memory (MTA-LSTM) network.In this model, we maintain a novel multi-topic coverage vector, which learns the weight of each topic and is sequentially updated during the decoding process.Afterwards this vector is fed to an attention model to guide the generator.Moreover, we automatically construct two paragraph-level Chinese essay corpora, 305,000 essay paragraphs and 55,000 question-and-answer pairs.Empirical results show that our approach obtains much better BLEU score compared to various baselines.Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.

Facing a large amount of entities appearing on the web, entity linking has recently become useful. It assigns an entity from a resource to one name mention to help users grasp the meaning of this name mention. Unfortunately, many possible entities can be assigned to one name mention. Apparently, the usually co-occurring name mentions are related and can be considered together to determine their best assignments. This approach is called collective entity linking and is often conducted based on entity graph. However, traditional collective entity linking methods either consume much time due to the large scale of entity graph or obtain low accuracy due to simplifying graph. To improve both accuracy and efficiency, this article proposes a novel collective entity linking algorithm. It first constructs an entity graph by connecting any two related entities, and then a probability-based objective function is proposed on this graph to ensure the high accuracy of the linking result. Via this function, we convert entity linking to the process of finding the nodes with the highest PageRank Values. Greedy search and an adjusted Monte Carlo random walk are proposed to fulfill this work. Experimental results demonstrate that our algorithm performs much better than traditional linking methods.

Writing academic articles in English is a challenging task for non-native speakers, as more effort has to be spent to enhance their language expressions. This paper presents an academic writing assistant system called Write-righter, which can provide real-time hint and recommendation by analyzing the input context. To achieve this goal, some novel strategies, e.g., semantic extension based sentence retrieval and LDA based sentence structure identification have been proposed. Write-righter is expected to help people express their ideas correctly by recommending top N most possible expressions.

Today, plenty of apps are released to help users make the best use of their mobile phones. Facing the large amount of apps, app retrieval and app recommendation are extensively adopted to help users obtain their favorite apps. To acquire the high-quality retrieval or recommending results, it needs to obtain the accurate app relationship calculating results in advance. Unfortunately, recent methods are conducted mostly depending on user's log or app's contexts, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many deep relationships other than similarity, e.g., one app needs another app to cooperate to fulfill its work. Obviously, app's reviews contain user's viewpoint. They are useful to help dig deep relationship between apps. Therefore, to calculate relationship between apps via reviews, we propose an iterative process by combining review similarity calculation and app relationship calculation together.

The link prediction problem in social networks is to estimate the value of the link that can represent relationship between social members. Researchers have proposed several methods for solving link prediction and a number of features have been used. Most of these models are learned with only considering the features from one kind of data. In this paper, by considering the data from link network structure and user comment, both of which could imply the concept of link value, we propose multimodal learning based approaches to predict the link values. The experiment results done on dataset from typical social networks show that our model could learn the joint representation of these datas properly, and the method MDBN outperforms other state-of-art link prediction methods.

Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending results, it needs to obtain the precise app relationship calculating results. Unfortunately, the recent methods are conducted mostly relying on user's log or app's description, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many general relationships other than similarity, such as one app needs another app as its tool. These relationships cannot be dug via user's log or app's description. Reviews contain user's viewpoint and judgment to apps, thus they can be used to calculate relationship between apps. To use reviews, this paper proposes an iterative process by combining review similarity and app relationship together. Experimental results demonstrate that via this iterative process, relationship between apps can be calculated exactly. Furthermore, this process is improved in two aspects. One is to obtain excellent results even with weak initialization. The other is to apply matrix product to reduce running time.

With the development of social media websites, more and more users start to show their attitudes and emotions to each other. Some of these interactions can be represented as links with sign values(positive or negative). In this paper, a unified method is proposed for link prediction and feature analysis. This paper focuses on the data from social media websites and tries to find the features that determine the sign value mostly. Based on the features extracted from the users' self statuses and from their relationships with neighbors, our method can predict the links' values with high accuracy. By analyzing the models generated over different datasets, our experiments find out the common determining features for link prediction. Based on our results, advices on how to predict links' values and get more positive links in future are given to users.