@inproceedings{li-etal-2023-diaasq,
title = "{D}ia{ASQ}: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis",
author = "Li, Bobo and
Fei, Hao and
Li, Fei and
Wu, Yuhan and
Zhang, Jinsong and
Wu, Shengqiong and
Li, Jingye and
Liu, Yijiang and
Liao, Lizi and
Chua, Tat-Seng and
Ji, Donghong",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.849",
doi = "10.18653/v1/2023.findings-acl.849",
pages = "13449--13467",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
%A Li, Bobo
%A Fei, Hao
%A Li, Fei
%A Wu, Yuhan
%A Zhang, Jinsong
%A Wu, Shengqiong
%A Li, Jingye
%A Liu, Yijiang
%A Liao, Lizi
%A Chua, Tat-Seng
%A Ji, Donghong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-diaasq
%X 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.
%R 10.18653/v1/2023.findings-acl.849
%U https://aclanthology.org/2023.findings-acl.849
%U https://doi.org/10.18653/v1/2023.findings-acl.849
%P 13449-13467
Markdown (Informal)
[DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://aclanthology.org/2023.findings-acl.849) (Li et al., Findings 2023)
ACL
- Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, and Donghong Ji. 2023. DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13449–13467, Toronto, Canada. Association for Computational Linguistics.