  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.
