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.

In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision.Furthermore, the system is not restricted to use a particular set of domain labels.We exploit the knowledge encoded within different off-theshelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition.The proposed zero-shot system achieves a new state-of-theart on the English dataset used in the evaluation.