Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-long.582 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Langedijk | 1 | 0 | 0.68 |
| Verna Dankers | 2 | 0 | 0.34 |
| Sander Bos | 3 | 0 | 0.34 |
| Bryan Cardenas Guevara | 4 | 0 | 0.34 |
| Helen Yannakoudakis | 5 | 154 | 13.22 |
| Ekaterina Shutova | 6 | 228 | 21.51 |