Abstract
Style transfer is the task of paraphrasing text into a target-style domain while retaining the content. Unsupervised approaches mainly focus on training a generator to rewrite input sentences. In this work, we assume that text styles are determined by only a small proportion of words; therefore, rewriting sentences via generative models may be unnecessary. As an alternative, we consider style transfer as a sequence tagging task. Specifically, we use edit operations (i.e., deletion, insertion and substitution) to tag words in an input sentence. We train a classifier and a language model to score tagged sequences and build a conditional random field. Finally, the optimal path in the conditional random field is used as the output. The results of experiments comparing models indicate that our proposed model exceeds end-to-end baselines in terms of accuracy on both sentiment and style transfer tasks with comparable or better content preservation.- Anthology ID:
- 2022.wassa-1.33
- Volume:
- Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani, João Sedoc, Roman Klinger, Alexandra Balahur
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 293–303
- Language:
- URL:
- https://aclanthology.org/2022.wassa-1.33
- DOI:
- 10.18653/v1/2022.wassa-1.33
- Cite (ACL):
- Yang Shuo. 2022. Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 293–303, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer (Shuo, WASSA 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.wassa-1.33.pdf
- Data
- GYAFC, IMDb Movie Reviews