Yuhan Wu
2023
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.
Search
Co-authors
- Bobo Li 1
- Hao Fei 1
- Fei Li 1
- Jinsong Zhang (张劲松) 1
- Shengqiong Wu 1
- show all...