Abstract
Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be surprisingly effective for cross-lingual transfer of syntactic parsing models Wu and Dredze (2019), but only between related languages. However, source and training languages are rarely related, when parsing truly low-resource languages. To close this gap, we adopt a method from multi-task learning, which relies on automated curriculum learning, to dynamically optimize for parsing performance on outlier languages. We show that this approach is significantly better than uniform and size-proportional sampling in the zero-shot setting.- Anthology ID:
- 2022.acl-short.64
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 578–587
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.64
- DOI:
- 10.18653/v1/2022.acl-short.64
- Cite (ACL):
- Miryam de Lhoneux, Sheng Zhang, and Anders Søgaard. 2022. Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 578–587, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (de Lhoneux et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.acl-short.64.pdf
- Code
- mdelhoneux/machamp-worst_case_acl