A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction

Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, Huijia Zhu


Abstract
Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.
Anthology ID:
2022.coling-1.616
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
7065–7074
Language:
URL:
https://aclanthology.org/2022.coling-1.616
DOI:
Bibkey:
Cite (ACL):
Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, and Huijia Zhu. 2022. A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7065–7074, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (Zhao et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.616.pdf