Language Models are the core for almost any Natural Language Processing system nowadays. One of their particularities is their contextualized representations, a game changer feature when a disambiguation between word senses is necessary. In this paper we aim to explore to what extent language models are capable of discerning among senses at inference time. We performed this analysis by prompting commonly used Languages Models such as BERT or RoBERTa to perform the task of Word Sense Disambiguation (WSD). We leverage the relation between word senses and domains, and cast WSD as a textual entailment problem, where the different hypothesis refer to the domains of the word senses. Our results show that this approach is indeed effective, close to supervised systems.

翻译：语言模型是当今几乎所有自然语言处理系统的核心。其特色之一在于上下文表示，这一特性在词义消歧需求中具有变革性意义。本文旨在探索语言模型在推理时区分词义的能力边界。我们通过提示常用语言模型（如BERT或RoBERTa）执行词义消歧任务来进行分析。我们利用词义与领域之间的关联，将词义消歧重构为文本蕴含问题，其中不同假设对应词义的各个领域。实验结果表明，该方法确实有效，性能接近有监督系统。