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We examine the inducement of rare but severe errors in English-Chinese andChinese-English in-domain neural machine translation by minimal deletion of thesource text with character-based models. By deleting a single character, we caninduce severe translation errors. We categorize these errors and compare theresults of deleting single characters and single words. We also examine theeffect of training data size on the number and types of pathological casesinduced by these minimal perturbations, finding significant variation. We findthat deleting a word hurts overall translation score more than deleting acharacter, but certain errors are more likely to occur when deletingcharacters, with language direction also influencing the effect.

Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English