State-of-the-art methods for Word Sense Disambiguation (WSD) combine two
different features: the power of pre-trained language models and a propagation
method to extend the coverage of such models. This propagation is needed as
current sense-annotated corpora lack coverage of many instances in the
underlying sense inventory (usually WordNet). At the same time, unambiguous
words make for a large portion of all words in WordNet, while being poorly
covered in existing sense-annotated corpora. In this paper, we propose a simple
method to provide annotations for most unambiguous words in a large corpus. We
introduce the UWA (Unambiguous Word Annotations) dataset and show how a
state-of-the-art propagation-based model can use it to extend the coverage and
quality of its word sense embeddings by a significant margin, improving on its
original results on WSD.

本文提出了一种简单的方法，为大多数无歧义单词提供注释，引入了 UWA（无歧义单词注释）数据集，并展示了一种最先进的基于传播的模型如何使用它来显著提高其词义嵌入覆盖范围和质量，从而改善了其在 WSD 上的原始结果。