  Human translators routinely have to translate rare inflections of words - due
to the Zipfian distribution of words in a language. When translating from
Spanish, a good translator would have no problem identifying the proper
translation of a statistically rare inflection such as habl\'aramos. Note the
lexeme itself, hablar, is relatively common. In this work, we investigate
whether state-of-the-art bilingual lexicon inducers are capable of learning
this kind of generalization. We introduce 40 morphologically complete
dictionaries in 10 languages and evaluate three of the state-of-the-art models
on the task of translation of less frequent morphological forms. We demonstrate
that the performance of state-of-the-art models drops considerably when
evaluated on infrequent morphological inflections and then show that adding a
simple morphological constraint at training time improves the performance,
proving that the bilingual lexicon inducers can benefit from better encoding of
morphology.
