Style transfer from non-parallel text by cross-alignment
© 2017 Neural information processing systems foundation. All rights reserved. This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. The key challenge is to sep...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021-11-05T12:28:34Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | © 2017 Neural information processing systems foundation. All rights reserved. This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. The key challenge is to separate the content from other aspects such as style. We assume a shared latent content distribution across different text corpora, and propose a method that leverages refined alignment of latent representations to perform style transfer. The transferred sentences from one style should match example sentences from the other style as a population. We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order. |
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