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.

我们提出一种新颖的形态学感知概率模型用于双语词典归纳，该模型以结构化方式联合建模词元翻译和屈折形态学。我们的模型利用词元是意义的关键词汇单位而屈折形态学提供额外句法信息的基本语言直觉。这种方法导致了显着的性能改进 - 在监督场景和弱监督场景下，跨 6 种语言对平均准确性提高了 19% 和 16%。作为另一个贡献，我们强调现代 BLI 所面临的忽视屈折形态学的问题，并提出三个改进任务的建议。