Bryan Cardenas Guevara


2022

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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
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.