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METNet: A Mutual Enhanced Transformation network for Aspect-based Sentiment Analysis 28

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METNet: A Mutual Enhanced Transformation Network for Aspect-...

28th International Conference on Computational Linguistics, COLING 2020

作者： Jiang, Bin Hou, Jing Zhou, Wanyue Yang, Chao Wang, Shihan Pang, Liang College of Computer Science and Electronic Engineering Hunan University China Intelligent Systems Utrecht University Netherlands CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Sentiment analysis

Detector development at the Back-n white neutron source

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Radiation Detection technology and Methods 2023年第2期7卷 171-191页

作者： Fan Ruirui Li Qiang Bao Jie Li Yang Liu Rong Jiang Wei Ren Jie Zhang Qiwei Cao Ping Gu Minhao Ren Zhizhou Yi Han Tang Jingyu An Qi Bai Haofan Bai Jiangbo Chen Qiping Chen Yonghao Chen Zhen Cui Zengqi Fan Anchuan Feng Changqing Feng Fanzhen Gao Keqing Han Changcai Han Zijie He Guozhu He Yongcheng Hong Yang Hu Yiwei Huang Hanxiong Jia Weihua Jiang Haoyu Jiang Zhijie Jin Zhengyao Kang Ling Li Bo Li Chao Li Gong Li Jiawen Li Xiao Liu Jie Liu Shubin Luan Guangyuan Ning Changjun Qi Binbin Ruan Xichao Song Zhaohui Sun Kang Tan Zhixin Tang Shengda Wang Pengcheng Wang Zhaohui Wen Zhongwei Wu Xiaoguang Wu Xuan Xie Likun Yang Yiwei Yu Yongji Zhang Guohui Zhang Linhao Zhang Mohan Zhang Xianpeng Zhang Yuliang Zhang Yue Zhang Zhiyong Zhao Maoyuan Zhou Luping Zhou Zhihao Zhu Kejun The CSNS Back-n Collaboration Institute of High Energy Physics CASBeijing 100049People’s Republic of China Spallation Neutron Source Science Center Dongguan 523803People’s Republic of China State Key Laboratory of Particle Detection and Electronics Beijing 100049People’s Republic of China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413People’s Republic of China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900People’s Republic of China Department of Modern Physics University of Science and Technology of ChinaHefei 230026People’s Republic of China State Key Laboratory of Particle Detection and Electronics Hefei 230026People’s Republic of China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871People’s Republic of China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026People’s Republic of China Northwest Institute of Nuclear Technology Xi’an 710024People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China 不详

Purpose Back-n is a white neutron beamline at China spallation neutron source,which was established in the year of *** is a powerful facility for nuclear data measurement,neutron detector calibration,and radiation effect *** A series of detectors were built for different experiments,including beam monitoring,beam profile measurement,neutron induced secondaries(fission fragments,light charged particles and gamma)cross section measurement,and neutron resonance radiography,etc.A common digitization electronics and a cluster-based DAQ were developed for these detector *** detectors have been employed at Back-n and serviced for experiments from the beginning of the beamline *** and conclusion As an overview of detectors of Back-n,the details of the detector design and the experiment performing are described in this *** developing systems,e.g.,MTPC and B-MCP,are also included.

关键词： White neutron source Beam diagnostics Fission fragment Charged particle Gamma Neutron resonance radiography

Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text

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arXiv 2023年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Zhao, Tianzhe Zhang, Jian Zhang, Lingling School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D National Engineering lab for Big Data Analytics Xi’an China

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. © 2023, CC BY.

关键词： Benchmarking

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

Link Prediction on n-ary relational data based on relatedness evaluation

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arXiv 2021年

作者： Guan, Saiping Jin, Xiaolong Guo, Jiafeng Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

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IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs

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arXiv 2021年

作者： Li, Zixuan Jin, Xiaolong Guan, Saiping Li, Wei Guo, Jiafeng Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Baidu Inc

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

Adaptive information seeking for open-domain question answering

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arXiv 2021年

作者： Zhu, Yunchang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step. However, almost all existing iterative approaches use predefined strategies, either applying the same retrieval function multiple times or fixing the order of different retrieval functions, which cannot fulfill the diverse requirements of various questions. In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. Specifically, the whole retrieval and answer process is modeled as a partially observed Markov decision process, where three types of retrieval operations (e.g., BM25, DPR, and hyperlink) and one answer operation are defined as actions. According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step, based on the collected evidence and the reformulated query, or directly output the answer when the evidence set is sufficient for the question. Experiments on SQuAD Open and HotpotQA fullwiki, which serve as single-hop and multi-hop open-domain QA benchmarks, show that AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations. © 2021, CC BY.

关键词： Hypertext systems

Sketch and customize: A counterfactual story generator

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arXiv 2021年

作者： Hao, Changying Pang, Liang Lan, Yanyan Wang, Yan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Tencent AI Lab United States

Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text. Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a model to predict the corresponding story ending when the condition is modified to a counterfactual one. Previous works have shown that the traditional sequence-to-sequence model cannot well handle this problem, as it often captures some spurious correlations between the original and counterfactual endings, instead of the causal relations between conditions and endings. To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending. In the customize stage, a generation model is used to fill proper words in the skeleton under the guidance of the counterfactual condition. In this way, the obtained counterfactual ending is both relevant to the original ending and consistent with the counterfactual condition. Experimental results show that the proposed model generates much better endings, as compared with the traditional sequence-to-sequence model. © 2021, CC BY-NC-ND.

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