Translating away Translationese without Parallel Data

• URL: http://arxiv.org/abs/2310.18830v1
• Date: Sat, 28 Oct 2023 22:11:25 GMT
• Title: Translating away Translationese without Parallel Data
• Authors: Rricha Jalota, Koel Dutta Chowdhury, Cristina Espa\~na-Bonet, Josef van Genabith
• Abstract summary: Translated texts exhibit systematic linguistic differences compared to original texts in the same language. In this paper, we explore a novel approach to reduce translationese in translated texts: translation-based style transfer. We show how we can eliminate the need for parallel validation data by combining the self-supervised loss with an unsupervised loss.
• Score: 14.423809260672877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Translated texts exhibit systematic linguistic differences compared to original texts in the same language, and these differences are referred to as translationese. Translationese has effects on various cross-lingual natural language processing tasks, potentially leading to biased results. In this paper, we explore a novel approach to reduce translationese in translated texts: translation-based style transfer. As there are no parallel human-translated and original data in the same language, we use a self-supervised approach that can learn from comparable (rather than parallel) mono-lingual original and translated data. However, even this self-supervised approach requires some parallel data for validation. We show how we can eliminate the need for parallel validation data by combining the self-supervised loss with an unsupervised loss. This unsupervised loss leverages the original language model loss over the style-transferred output and a semantic similarity loss between the input and style-transferred output. We evaluate our approach in terms of original vs. translationese binary classification in addition to measuring content preservation and target-style fluency. The results show that our approach is able to reduce translationese classifier accuracy to a level of a random classifier after style transfer while adequately preserving the content and fluency in the target original style.

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