  Sentence embedding methods using natural language inference (NLI) datasets
have been successfully applied to various tasks. However, these methods are
only available for limited languages due to relying heavily on the large NLI
datasets. In this paper, we propose DefSent, a sentence embedding method that
uses definition sentences from a word dictionary, which performs comparably on
unsupervised semantics textual similarity (STS) tasks and slightly better on
SentEval tasks than conventional methods. Since dictionaries are available for
many languages, DefSent is more broadly applicable than methods using NLI
datasets without constructing additional datasets. We demonstrate that DefSent
performs comparably on unsupervised semantics textual similarity (STS) tasks
and slightly better on SentEval tasks to the methods using large NLI datasets.
Our code is publicly available at https://github.com/hpprc/defsent .
