Jinsong Zhang


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DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
Bobo Li | Hao Fei | Fei Li | Yuhan Wu | Jinsong Zhang | Shengqiong Wu | Jingye Li | Yijiang Liu | Lizi Liao | Tat-Seng Chua | Donghong Ji
Findings of the Association for Computational Linguistics: ACL 2023

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.


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基于GPT-2和互信息的语言单位信息量对韵律特征的影响(Prosodic Effects of Speech Unit’s Information Based on GPT-2 and Mutual Information)
Yun Hao (郝韵) | Yanlu Xie (解焱陆) | Binghuai Lin (林炳怀) | Jinsong Zhang (张劲松)
Proceedings of the 21st Chinese National Conference on Computational Linguistics



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Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems
Xiaoyun Wang | Jinsong Zhang | Masafumi Nishida | Seiichi Yamamoto
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with mispronunciation by second language speakers. In this study, we used a phonetic decision tree (PDT)-based top-down sequential splitting method to generate the reduced phoneme set and then applied this method to a translation-game type English CALL system for Japanese to determine its effectiveness. Experimental results showed that the proposed method improves the performance of recognizing non-native speech.