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Human translators routinely have to translate rare inflections of words - dueto the Zipfian distribution of words in a language. When translating fromSpanish, a good translator would have no problem identifying the propertranslation of a statistically rare inflection such as habl\'aramos. Note thelexeme itself, hablar, is relatively common. In this work, we investigatewhether state-of-the-art bilingual lexicon inducers are capable of learningthis kind of generalization. We introduce 40 morphologically completedictionaries in 10 languages and evaluate three of the state-of-the-art modelson the task of translation of less frequent morphological forms. We demonstratethat the performance of state-of-the-art models drops considerably whenevaluated on infrequent morphological inflections and then show that adding asimple morphological constraint at training time improves the performance,proving that the bilingual lexicon inducers can benefit from better encoding ofmorphology.

Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction