Abstract
Flipping sentiment while preserving sentence meaning is challenging because parallel sentences with the same content but different sentiment polarities are not always available for model learning. We introduce a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose a model that learns to minimize the sentiment and content losses in a fully end-to-end manner. Our model is simple and offers well-balanced results across two domains: Yelp restaurant and Amazon product reviews.- Anthology ID:
- D19-1659
- 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:
- 6311–6316
- Language:
- URL:
- https://aclanthology.org/D19-1659
- DOI:
- 10.18653/v1/D19-1659
- Cite (ACL):
- Canasai Kruengkrai. 2019. Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora. 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 6311–6316, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora (Kruengkrai, EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D19-1659.pdf