  We explore recently introduced definition modeling technique that provided
the tool for evaluation of different distributed vector representations of
words through modeling dictionary definitions of words. In this work, we study
the problem of word ambiguities in definition modeling and propose a possible
solution by employing latent variable modeling and soft attention mechanisms.
Our quantitative and qualitative evaluation and analysis of the model shows
that taking into account words ambiguity and polysemy leads to performance
improvement.
