Skip to main content Skip to main navigation


Towards Debiasing Translation Artifacts

Koel Dutta Chowdhury; Rricha Jalota; Cristina España-Bonet; Josef van Genabith
In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL-2022), Seattle, United States, Pages 3983-3991, Association for Computational Linguistics, 2022.


Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets. However, compared to original texts in the same language, translations possess distinct qualities referred to as translationese. Previous research has shown that these translation artifacts influence the performance of a variety of cross-lingual tasks. In this work, we propose a novel approach to reducing translationese by extending an established bias-removal technique. We use the Iterative Null-space Projection (INLP) algorithm, and show by measuring classification accuracy before and after debiasing, that translationese is reduced at both sentence and word level. We evaluate the utility of debiasing translationese on a natural language inference (NLI) task, and show that by reducing this bias, NLI accuracy improves. To the best of our knowledge, this is the first study to debias translationese as represented in latent embedding space.

Weitere Links