“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer

Akhilesh Sudhakar, Bhargav Upadhyay, Arjun Maheswaran

Abstract
Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger ‘Delete Retrieve Generate’ framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.
Anthology ID:
D19-1322
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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3269–3279
Language:
URL:
https://aclanthology.org/D19-1322
DOI:
10.18653/v1/D19-1322
Bibkey:
Cite (ACL):
Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. 2019. “Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer. 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 3269–3279, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (Sudhakar et al., EMNLP-IJCNLP 2019)
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