Improved Neural Text Attribute Transfer with Non-parallel Data

Abstract

Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes. In this work, we propose multiple improvements over the existing approaches that enable the encoder-decoder framework to cope with the text attribute transfer from non-parallel data. We perform experiments on the sentiment transfer task using two datasets. For both datasets, our proposed method outperforms a strong baseline in two of the three employed evaluation metrics.

NIPS 2017 Workshop on Learning Disentangled Representations: from Perception to Control

References in corpus (2)

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Effective Approaches to Attention-based Neural Machine Translation

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