METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis

Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, Liang Pang


Abstract
Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios.
Anthology ID:
2020.coling-main.14
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
162–172
Language:
URL:
https://aclanthology.org/2020.coling-main.14
DOI:
10.18653/v1/2020.coling-main.14
Bibkey:
Cite (ACL):
Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, and Liang Pang. 2020. METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 162–172, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (Jiang et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/author-url/2020.coling-main.14.pdf