Abstract
In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants. We introduce a pipeline called SMERTI that combines entity replacement, similarity masking, and text infilling. We measure our pipeline’s success by its Semantic Text Exchange Score (STES): the ability to preserve the original text’s sentiment and fluency while adjusting semantic content. We propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. Our experiments demonstrate that SMERTI can outperform baseline models on Yelp reviews, Amazon reviews, and news headlines.- Anthology ID:
- D19-1272
- 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:
- 2701–2711
- Language:
- URL:
- https://aclanthology.org/D19-1272
- DOI:
- 10.18653/v1/D19-1272
- Cite (ACL):
- Steven Y. Feng, Aaron W. Li, and Jesse Hoey. 2019. Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange. 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 2701–2711, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (Feng et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D19-1272.pdf
- Code
- styfeng/SMERTI