Abstract
Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.- Anthology ID:
- 2021.acl-long.154
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1978–1992
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.154
- DOI:
- 10.18653/v1/2021.acl-long.154
- Cite (ACL):
- M Saiful Bari, Tasnim Mohiuddin, and Shafiq Joty. 2021. UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1978–1992, Online. Association for Computational Linguistics.
- Cite (Informal):
- UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP (Bari et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.acl-long.154.pdf
- Data
- PAWS-X, XNLI