Abstract
In this work, we explore the novel idea of employing dependency parsing information in the context of few-shot learning, the task of learning the meaning of a rare word based on a limited amount of context sentences. Firstly, we use dependency-based word embedding models as background spaces for few-shot learning. Secondly, we introduce two few-shot learning methods which enhance the additive baseline model by using dependencies.- Anthology ID:
- 2022.acl-srw.38
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Samuel Louvan, Andrea Madotto, Brielen Madureira
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 461–466
- Language:
- URL:
- https://aclanthology.org/2022.acl-srw.38
- DOI:
- 10.18653/v1/2022.acl-srw.38
- Cite (ACL):
- Stefania Preda and Guy Emerson. 2022. Using dependency parsing for few-shot learning in distributional semantics. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 461–466, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Using dependency parsing for few-shot learning in distributional semantics (Preda & Emerson, ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-srw.38.pdf