Sentiment Classification towards Question-Answering with Hierarchical Matching Network

Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou


Abstract
In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.
Anthology ID:
D18-1401
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3654–3663
Language:
URL:
https://aclanthology.org/D18-1401
DOI:
10.18653/v1/D18-1401
Bibkey:
Cite (ACL):
Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2018. Sentiment Classification towards Question-Answering with Hierarchical Matching Network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3654–3663, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (Shen et al., EMNLP 2018)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/D18-1401.pdf
Video:
 https://vimeo.com/306126825