Abstract
Recent multilingual models benefit from strong unified semantic representation models. However, due to conflict linguistic regularities, ignoring language-specific features during multilingual learning may suffer from negative transfer. In this work, we analyze the relationbetween a language’s position space and its typological characterization, and suggest deploying different position spaces for different languages. We develop a position generation network which combines prior knowledge from typology features and existing position vectors. Experiments on the multilingual dependency parsing task show that the learned position vectors exhibit meaningful hidden structures, and they can help achieving the best multilingual parsing results.- Anthology ID:
- 2023.findings-acl.854
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13524–13541
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.854
- DOI:
- 10.18653/v1/2023.findings-acl.854
- Cite (ACL):
- Tao Ji, Yuanbin Wu, and Xiaoling Wang. 2023. Typology Guided Multilingual Position Representations: Case on Dependency Parsing. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13524–13541, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Typology Guided Multilingual Position Representations: Case on Dependency Parsing (Ji et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.findings-acl.854.pdf