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2019 EMNLP EMNLP 2019

Decomposing Textual Information For Style Transfer

AbstractThis 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

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