Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
Abstract
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual dependency parsing. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer and standard supervised learning baselines for a variety of unseen, typologically diverse, and low-resource languages, in a few-shot learning setup.- Anthology ID:
- 2022.acl-long.582
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8503–8520
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.582
- DOI:
- 10.18653/v1/2022.acl-long.582
- Cite (ACL):
- Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, and Ekaterina Shutova. 2022. Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8503–8520, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (Langedijk et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.acl-long.582.pdf
- Code
- annaproxy/udify-metalearning