Don't lose the message while paraphrasing: A study on content preserving style transfer

• URL: http://arxiv.org/abs/2308.09055v1
• Date: Thu, 17 Aug 2023 15:41:08 GMT
• Title: Don't lose the message while paraphrasing: A study on content preserving style transfer
• Authors: Nikolay Babakov, David Dale, Ilya Gusev, Irina Krotova, Alexander Panchenko
• Abstract summary: Content preservation is critical for real-world applications of style transfer studies. We compare various style transfer models on the example of the formality transfer domain. We conduct a precise comparative study of several state-of-the-art techniques for style transfer.
• Score: 61.38460184163704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text style transfer techniques are gaining popularity in natural language processing allowing paraphrasing text in the required form: from toxic to neural, from formal to informal, from old to the modern English language, etc. Solving the task is not sufficient to generate some neural/informal/modern text, but it is important to preserve the original content unchanged. This requirement becomes even more critical in some applications such as style transfer of goal-oriented dialogues where the factual information shall be kept to preserve the original message, e.g. ordering a certain type of pizza to a certain address at a certain time. The aspect of content preservation is critical for real-world applications of style transfer studies, but it has received little attention. To bridge this gap we perform a comparison of various style transfer models on the example of the formality transfer domain. To perform a study of the content preservation abilities of various style transfer methods we create a parallel dataset of formal vs. informal task-oriented dialogues. The key difference between our dataset and the existing ones like GYAFC [17] is the presence of goal-oriented dialogues with predefined semantic slots essential to be kept during paraphrasing, e.g. named entities. This additional annotation allowed us to conduct a precise comparative study of several state-of-the-art techniques for style transfer. Another result of our study is a modification of the unsupervised method LEWIS [19] which yields a substantial improvement over the original method and all evaluated baselines on the proposed task.

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