Evaluating a Multi-sense Definition Generation Model for Multiple Languages
This addresses the challenge of polysemy in definition generation for multiple languages, though it appears incremental as it builds on existing multi-sense embedding techniques.
The authors tackled the problem of definition modeling for polysemous words by proposing a context-agnostic approach using multi-sense word embeddings to generate multiple definitions per word. Their model outperformed a single-sense baseline on all fifteen datasets across nine languages, as measured by BLEU variations.
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.