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
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.
BibTeX
@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"
}