Definition modeling is a recently-introduced task in natural language processing (NLP) which aims to predict and generate dictionary-style definitions for any given word. Most prior work on definition modelling has not accounted for polysemy — i.e. a linguistic phenomenon in which a word can imply multiple meanings when used in various contexts — or has done so by considering definition modelling for a target word in a given context. In contrast, in this study, we propose a context-agnostic approach to definition modelling, 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 — i.e., a widely-used evaluation metric initially introduced for machine translation that is adapted to definition modeling. Our results demonstrate that our proposed multisense model outperforms a single-sense model on all fifteen datasets.
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