  We propose a novel morphologically aware probability model for bilingual
lexicon induction, which jointly models lexeme translation and inflectional
morphology in a structured way. Our model exploits the basic linguistic
intuition that the lexeme is the key lexical unit of meaning, while
inflectional morphology provides additional syntactic information. This
approach leads to substantial performance improvements - 19% average
improvement in accuracy across 6 language pairs over the state of the art in
the supervised setting and 16% in the weakly supervised setting. As another
contribution, we highlight issues associated with modern BLI that stem from
ignoring inflectional morphology, and propose three suggestions for improving
the task.
