  Word embeddings capture syntactic and semantic information about words.
Definition modeling aims to make the semantic content in each embedding
explicit, by outputting a natural language definition based on the embedding.
However, existing definition models are limited in their ability to generate
accurate definitions for different senses of the same word. In this paper, we
introduce a new method that enables definition modeling for multiple senses. We
show how a Gumble-Softmax approach outperforms baselines at matching
sense-specific embeddings to definitions during training. In experiments, our
multi-sense definition model improves recall over a state-of-the-art
single-sense definition model by a factor of three, without harming precision.
