@inproceedings{sainz-rigau-2021-ask2transformers,
    title = "{A}sk2{T}ransformers: Zero-Shot Domain labelling with Pretrained Language Models",
    author = "Sainz, Oscar  and
      Rigau, German",
    editor = "Vossen, Piek  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.gwc-1.6/",
    pages = "44--52",
    abstract = "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-the-shelf 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-the-art on the English dataset used in the evaluation."
}