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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Ziliang Dou, Zhicheng Mao, Jiaxin Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450387323

Asking clarifying questions is an interactive way to effectively clarify user intent. When a user submits a query, the search engine will return a clarifying question with several clickable items of sub-intents for clarification. According to the existing definition, the key to asking high-quality questions is to generate good descriptions for submitted queries and provided items. However, existing methods mainly based on static knowledge bases are difficult to find descriptions for many queries because of the lack of entities within these queries and their corresponding items. For such a query, it is unable to generate an informative question. To alleviate this problem, we propose leveraging top search results of the query to help generate better descriptions because we deem that the top retrieved documents contain rich and relevant contexts of the query. Specifically, we first design a rule-based algorithm to extract description candidates from search results and rank them by various human-designed features. Then, we apply an learning-to-rank model and another generative model for generalization and further improve the quality of clarifying questions. Experimental results show that our proposed methods can generate more readable and informative questions compared with existing methods. The results prove that search results can be utilized to improve users' search experience for search clarification in conversational search systems.

关键词： Clarifying Question Search Clarification Conversational Search

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RLPS: A Reinforcement Learning-Based Framework for Personalized Search

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2021年第3期39卷 p1-29页

作者： Yao, Jing Dou, Zhicheng Xu, Jun Wen, Ji-Rong Renmin Univ China Sch Informat 59 Zhongguancun St Beijing 100872 Peoples R China Renmin Univ China Sch Artificial Intelligence 59 Zhongguancun St Beijing 100872 Peoples R China MOE Beijing Key Lab Big Data Management & Anal Method Key Lab Data Engn & Knowledge Engn 59 Zhongguancun St Beijing 100872 Peoples R China

Personalized search is a promising way to improve search qualities by taking user interests into consideration. Recently, machine learning and deep learning techniques have been successfully applied to search result personalization. Most existing models simply regard the personal search history as a static set of user behaviors and learn fixed ranking strategies based on all the recorded data. Though improvements have been achieved, the essence that the search process is a sequence of interactions between the search engine and user is ignored. The user's interests may dynamically change during the search process, therefore, it would be more helpful if a personalized search model could track the whole interaction process and adjust its ranking strategy continuously. In this article, we adapt reinforcement learning to personalized search and propose a framework, referred to as RLPS. It utilizes a Markov Decision Process (MDP) to track sequential interactions between the user and search engine, and continuously update the underlying personalized ranking model with the user's real-time feedback to learn the user's dynamic interests. Within this framework, we implement two models: the listwise RLPS-L and the hierarchical RLPS-H. RLPS-L interacts with users and trains the ranking model with document lists, while RLPS-H improves model training by designing a layered structure and introducing document pairs. In addition, we also design a feedback-aware personalized ranking component to capture the user's feedback, which impacts the user interest profile for the next query. Significant improvements over existing personalized search models are observed in the experiments on the public AOL search log and a commercial log.

关键词： Personalized search reinforcement learning Markov decision process (MDP)

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Natural Language Explainable Recommendation with Robustness Enhancement 24

Natural Language Explainable Recommendation with Robustness ...

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30th ACM SIGKDD Conference on Knowledge Discovery and data Mining

作者： Zhang, Jingsen Tang, Jiakai Chen, Xu Yu, Wenhui Hu, Lantao Jiang, Peng Li, Han Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9798400704901

Natural language explainable recommendation has become a promising direction to facilitate more efficient and informed user decisions. Previous models mostly focus on how to enhance the explanation accuracy. However, the robustness problem has been largely ignored, which requires the explanations generated for similar user-item pairs should not be too much different. Different from traditional classification problems, improving the robustness of natural languages has two unique characteristics: (1) Different token importances, that is, different tokens play various roles in representing the complete sentence, and the robustness requirements for predicting them should also be different. (2) Continuous token semantics, that is, the similarity of the output should be judged based on semantics, and the sequences without any token-level overlap may also be highly similar. Based on these characteristics, we formulate and solve a novel problem in the recommendation domain, that is, robust natural language explainable recommendation. To the best of our knowledge, it is the first time in this field. Specifically, we base our modeling on adversarial robust optimization and design four types of heuristic methods to modify the adversarial outputs with weighted token probabilities and synonym replacements. Furthermore, to consider the mutual influence between the above characteristics, we regard language generation as a decision-making problem and design a dual-policy reinforcement learning framework to improve the robustness of the generated languages. We conduct extensive experiments to demonstrate the effectiveness of our framework.

关键词： Explainable Recommendation Natural Language Explanations Adversarial Learning

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Looking Back on the Past: Active Learning With Historical Evaluation Results

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2022年第10期34卷 4921-4932页

作者： Yao, Jing Dou, Zhicheng Nie, Jian-Yun Wen, Ji-Rong Remmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method DEKE Beijing 100872 Peoples R China Univ Montreal DIRO Montreal PQ H3C 3J7 Canada

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of historical results and the fluctuation of the historical evaluation sequence, to improve the effectiveness of active learning sampling. Next, to further and more globally use the information contained in the historical results, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. We test our approaches on two common NLP tasks including text classification and named entity recognition. Experimental results show that our methods significantly promote existing methods.

关键词： Training Task analysis data models Uncertainty Entropy Text recognition Predictive models Active learning historical evaluation results named entity recognition text classification

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Recursive Visual Attention in Visual Dialog 32

Recursive Visual Attention in Visual Dialog

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32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Niu, Yulei Zhang, Hanwang Zhang, Manli Zhang, Jianhong Lu, Zhiwu Wen, Ji-Rong Renmin Univ China Beijing Key Lab Big Data Management & Anal Method Sch Informat Beijing 100872 Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9781728132938

Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA);(2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (e.g., "they") in the question (e.g., "Are they on or off?") are linked with nouns (e.g., "lamps") appearing in the dialog history (e.g., "How many lamps are there?") and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations.

关键词： Vision + Language Visual Reasoning

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Distribution-Aware Crowdsourced Entity Collection

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2019年第7期31卷 1312-1326页

作者： Fan, Ju Wei, Zhewei Zhang, Dongxiang Yang, Jingru Du, Xiaoyong Renmin Univ China Key Lab Data Engn & Knowledge Engn Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610051 Sichuan Peoples R China

The problem of crowdsourced entity collection solicits people (a.k.a. workers) to complete missing data in a database and has witnessed many applications in knowledge base completion and enterprise data collection. Although previous studies have attempted to address the "open world" challenge of crowdsourced entity collection, they do not pay much attention to the "distribution" of the collected entities. Evidently, in many real applications, users may have distribution requirements on the collected entities, e.g., even spatial distribution when collecting points-of-interest. In this paper, we study a new research problem, distribution-aware crowdsourced entity collection (CROWDDEC): Given an expected distribution w.r.t. an attribute (e.g., region or year), it aims to collect a set of entities via crowdsourcing and minimize the difference of the entity distribution from the expected distribution. Due to the openness of crowdsourcing, the CROWDDEC problem calls for effective crowdsourcing quality control. We propose an adaptive worker selection approach to address this problem. The approach estimates underlying entity distribution of workers on-the-fly based on the collected entities. Then, it adaptively selects the best set of workers that minimizes the difference from the expected distribution. Once workers submit their answers, it adjusts the estimation of workers' underlying distributions for subsequent adaptive worker selections. We prove the hardness of the problem, and develop effective estimation techniques as well as efficient worker selection algorithms to support this approach. We deployed the proposed approach on Amazon Mechanical Turk and the experimental results on two real datasets show that the approach achieves superiority on both effectiveness and efficiency.

关键词： Crowdsourcing entity collection sampling distribution-aware

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Teaching Machines to Extract Main Content for Machine Reading Comprehension 33

Teaching Machines to Extract Main Content for Machine Readin...

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33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Li, Zhaohui Feng, Yue Xu, Jun Guo, Jiafeng Lan, Yanyan Cheng, Xueqi Chinese Acad Sci CAS Key Lab Network Data Sci & Technol Inst Comp Technol Beijing Peoples R China Renmin Univ China Sch Informat Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781577358091

Machine reading comprehension, whose goal is to find answers from the candidate passages for a given question, has attracted a lot of research efforts in recent years. One of the key challenge in machine reading comprehension is how to identify the main content from a large, redundant, and overlapping set of candidate sentences. In this paper we propose to tackle the challenge with Markov Decision Process in which the main content identification is formalized as sequential decision making and each action corresponds to selecting a sentence. Policy gradient is used to learn the model parameters. Experimental results based on MSMARCO showed that the proposed model, called MC-MDP, can select high quality main contents and significantly improved the performances of answer span prediction.

关键词： Markov processes

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Distribution Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 721-733页

作者： Yu, Weijie Xu, Chen Xu, Jun Pang, Liang Wen, Ji-Rong Renmin Univ China Sch Informat Beijing 100872 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

Projecting the input text pair into a common semantic space where the matching function can be readily learned is an essential step for asymmetrical text matching. In the practice, it is often observed that the feature vectors from asymmetrical texts show a tendency to be gradually undistinguishable in the semantic space as the model is trained. However, the phenomenon is overlooked in existing studies. As a result, the feature vectors are constructed without any regularization, which inevitably hinders the learning of the downstream matching functions. In this paper, we first exploit the phenomenon and propose DDR-Match, a novel matching framework tailored for asymmetrical text matching. Specifically, in DDR-Match, a distribution distance-based regularizer is devised to accelerate the fusion of sequence representations corresponding to different domains in the semantic space. Then, we provide three instances of DDR-Match and make a comparison among them. DDR-Match is compatible with existing text matching methods by incorporating them as the underlying matching model. Four popular text matching methods are exploited in the paper. Extensive experimental results based on five publicly available benchmarks showed that DDR-Match consistently outperformed its underlying methods.

关键词： Semantics Neural networks Training Task analysis Measurement Speech processing Electronic mail Text matching sequence representation natural language processing

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Debiased Contrastive Learning of Unsupervised Sentence Representations 60

Debiased Contrastive Learning of Unsupervised Sentence Repre...

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60th Annual Meeting of the Association-for-Computational-Linguistics (ACL)

作者： Zhou, Kun Zhang, Beichen Zhao, Wayne Xin Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781955917216

Recently, contrastive learning has been shown to be effective in improving pre-trained language models (PLM) to derive high-quality sentence representations. It aims to pull close positive examples to enhance the alignment while push apart irrelevant negatives for the uniformity of the whole representation space. However, previous works mostly adopt in-batch negatives or sample from training data at random. Such a way may cause the sampling bias that improper negatives (e.g., false negatives and anisotropy representations) are used to learn sentence representations, which will hurt the uniformity of the representation space. To address it, we present a new framework DCLR (Debiased Contrastive Learning of unsupervised sentence Representations) to alleviate the influence of these improper negatives. In DCLR, we design an instance weighting method to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space. Experiments on seven semantic textual similarity tasks show that our approach is more effective than competitive baselines. Our code and data are publicly available at the link: https://***/RUCAIBox/DCLR.

关键词： Semantics

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Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing

Enhancing Scalability of Pre-trained Language Models via Eff...

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Conference on Empirical methods in Natural Language Processing (EMNLP)

作者： Liu, Peiyu Gao, Ze-Feng Chen, Yushuo Zhao, Wayne Xin Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Renmin Univ China Dept Phys Beijing Peoples R China

ISBN: (纸本)9798891760615

In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a more capable parameter-sharing architecture based on matrix product operator (MPO), an efficient tensor decomposition method to factorize the parameter matrix into a set of local tensors. Based on such a decomposition, we share the important local tensor across all layers for reducing the model size and meanwhile keep layer-specific tensors (also using Adapters) for enhancing the adaptation flexibility. To improve the model training, we further propose a stable initialization algorithm tailored for the MPO-based architecture. Extensive experiments have demonstrated the effectiveness of our proposed model in enhancing scalability and achieving higher performance (i.e., with fewer parameters than BERTBASE, we successfully scale the model depth by a factor of 4x and even achieve 0.1 points higher than BERTLARGE for GLUE score). The code to reproduce the results of this paper can be found at https: //***/RUCAIBox/MPOBERT-code.

关键词： Tensors

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