COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction

Zepeng Zhai, Hao Chen, Fangxiang Feng, Ruifan Li, Xiaojie Wang


Abstract
Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. In this paper, we propose a novel COntext-Masked MRC (COM-MRC) framework for ASTE. Our COM-MRC framework comprises three closely-related components: a context augmentation strategy, a discriminative model, and an inference method. Specifically, a context augmentation strategy is designed by enumerating all masked contexts for each aspect term. The discriminative model comprises four modules, i.e., aspect and opinion extraction modules, sentiment classification and aspect detection modules. In addition, a two-stage inference method first extracts all aspects and then identifies their opinions and sentiment through iteratively masking the aspects. Extensive experimental results on benchmark datasets show the effectiveness of our proposed COM-MRC framework, which outperforms state-of-the-art methods consistently.
Anthology ID:
2022.emnlp-main.212
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3230–3241
Language:
URL:
https://aclanthology.org/2022.emnlp-main.212
DOI:
10.18653/v1/2022.emnlp-main.212
Bibkey:
Cite (ACL):
Zepeng Zhai, Hao Chen, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. 2022. COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3230–3241, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (Zhai et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.212.pdf