  Most prior work on definition modeling has not accounted for polysemy, or has
done so by considering definition modeling for a target word in a given
context. In contrast, in this study, we propose a context-agnostic approach to
definition modeling, based on multi-sense word embeddings, that is capable of
generating multiple definitions for a target word. In further, contrast to most
prior work, which has primarily focused on English, we evaluate our proposed
approach on fifteen different datasets covering nine languages from several
language families. To evaluate our approach we consider several variations of
BLEU. Our results demonstrate that our proposed multi-sense model outperforms a
single-sense model on all fifteen datasets.
