  Popular word embedding methods such as word2vec and GloVe assign a single
vector representation to each word, even if a word has multiple distinct
meanings. Multi-sense embeddings instead provide different vectors for each
sense of a word. However, they typically cannot serve as a drop-in replacement
for conventional single-sense embeddings, because the correct sense vector
needs to be selected for each word. In this work, we study the effect of
multi-sense embeddings on the task of reverse dictionaries. We propose a
technique to easily integrate them into an existing neural network architecture
using an attention mechanism. Our experiments demonstrate that large
improvements can be obtained when employing multi-sense embeddings both in the
input sequence as well as for the target representation. An analysis of the
sense distributions and of the learned attention is provided as well.
