Decomposing textual information for style transfer

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Abstract

This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.

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APA

Yamshchikov, I. P., Shibaev, V., Nagaev, A., Jost, J., & Tikhonov, A. (2019). Decomposing textual information for style transfer. In EMNLP-IJCNLP 2019 - Proceedings of the 3rd Workshop on Neural Generation and Translation (pp. 128–137). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d19-5613

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