In this article, we tackle the issue of the limited quantity of manually
sense annotated corpora for the task of word sense disambiguation, by
exploiting the semantic relationships between senses such as synonymy,
hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton
WordNet, and thus reduce the number of different sense tags that must be
observed to disambiguate all words of the lexical database. We propose two
different methods that greatly reduces the size of neural WSD models, with the
benefit of improving their coverage without additional training data, and
without impacting their precision. In addition to our method, we present a WSD
system which relies on pre-trained BERT word vectors in order to achieve
results that significantly outperform the state of the art on all WSD
evaluation tasks.

提出两种不同的方法来压缩 Princeton WordNet 的词义词汇，以及基于预训练的 BERT 词向量建立的 WSD 系统，在不需要额外训练数据的情况下大幅减小了神经 WSD 模型的大小，并在所有 WSD 评估任务上显著优于现有技术水平。