Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer

Yang Shuo


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.33.pdf
Video:
 https://aclanthology.org/2022.wassa-1.33.mp4
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