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|a Jaakkola, Tommi
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|a Barzilay, Regina
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|a Lei, Tao
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|a Shen, Tianxiao
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|a Style transfer from non-parallel text by cross-alignment
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|c 2021-11-05T12:28:34Z.
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|u https://hdl.handle.net/1721.1/137438
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|a © 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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