Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
This addresses the challenge of adapting NLP models to low-resource languages, though it is incremental as it builds on existing meta-learning techniques.
The paper tackled the problem of resource scarcity in cross-lingual dependency parsing by applying meta-learning to enable fast adaptation to new languages, resulting in significant performance improvements over baselines for unseen, low-resource languages in a few-shot setup.
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.