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.
Cite
CITATION STYLE
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
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.