Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification

Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu Sun

Abstract
The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data. Most previous work follows a two-step process. They first separate the content from the original sentiment, and then directly generate text with the target sentiment only based on the content produced by the first step. However, the second step bears both the target sentiment addition and content reconstruction, thus resulting in a lack of specific information like proper nouns in the generated text. To remedy this, we propose a specificity-driven cascading approach in this work, which can effectively increase the specificity of the generated text and further improve content preservation. In addition, we propose a more reasonable metric to evaluate sentiment modification. The experiments show that our approach outperforms competitive baselines by a large margin, which achieves 11% and 38% relative improvements of the overall metric on the Yelp and Amazon datasets, respectively.
Anthology ID:
D19-1553
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5508–5517
Language:
URL:
https://aclanthology.org/D19-1553
DOI:
10.18653/v1/D19-1553
Bibkey:
Cite (ACL):
Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, and Xu Sun. 2019. Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5508–5517, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (Yang et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/add_acl24_videos/D19-1553.pdf
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