Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

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Abstract
In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.
Anthology ID:
P19-1345
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3548–3557
Language:
URL:
https://aclanthology.org/P19-1345
DOI:
10.18653/v1/P19-1345
Bibkey:
Cite (ACL):
Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2019. Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3548–3557, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (Wang et al., ACL 2019)
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