We examine the inducement of rare but severe errors in English-Chinese and
Chinese-English in-domain neural machine translation by minimal deletion of the
source text with character-based models. By deleting a single character, we can
induce severe translation errors. We categorize these errors and compare the
results of deleting single characters and single words. We also examine the
effect of training data size on the number and types of pathological cases
induced by these minimal perturbations, finding significant variation. We find
that deleting a word hurts overall translation score more than deleting a
character, but certain errors are more likely to occur when deleting
characters, with language direction also influencing the effect.Comment: COLING 2022 Camera Read