A Multi-task Learning Framework for Opinion Triplet Extraction

Chen Zhang, Qiuchi Li, Dawei Song, Benyou Wang


Abstract
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.
Anthology ID:
2020.findings-emnlp.72
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
819–828
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.72
DOI:
10.18653/v1/2020.findings-emnlp.72
Bibkey:
Cite (ACL):
Chen Zhang, Qiuchi Li, Dawei Song, and Benyou Wang. 2020. A Multi-task Learning Framework for Opinion Triplet Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 819–828, Online. Association for Computational Linguistics.
Cite (Informal):
A Multi-task Learning Framework for Opinion Triplet Extraction (Zhang et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.72.pdf
Code
 GeneZC/OTE-MTL +  additional community code