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.- Anthology ID:
- 2021.gwc-1.6
- Volume:
- Proceedings of the 11th Global Wordnet Conference
- Month:
- January
- Year:
- 2021
- Address:
- University of South Africa (UNISA)
- Editors:
- Piek Vossen, Christiane Fellbaum
- Venue:
- GWC
- SIG:
- SIGLEX
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 44–52
- Language:
- URL:
- https://aclanthology.org/2021.gwc-1.6
- DOI:
- Cite (ACL):
- Oscar Sainz and German Rigau. 2021. Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models. In Proceedings of the 11th Global Wordnet Conference, pages 44–52, University of South Africa (UNISA). Global Wordnet Association.
- Cite (Informal):
- Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models (Sainz & Rigau, GWC 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.gwc-1.6.pdf
- Code
- osainz59/Ask2Transformers
- Data
- MultiNLI