Transfer Text from one Style to Another
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Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain.
Text style transfer (TST) is an important task in natural language generation (NLG), which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.
This article aims to provide a comprehensive review of recent research efforts on text style transfer.
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
Ranked #6 on Text Style Transfer on Yelp Review Dataset (Small)
In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Ranked #1 on Unsupervised Text Style Transfer on Yelp
We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties.
Ranked #10 on Unsupervised Text Style Transfer on Yelp
We prove that this simple modification guides the latent space geometry of the resulting model by encouraging the encoder to map similar texts to similar latent representations.
Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.