Multilingual and cross-lingual document classification: A meta-learning approach
Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova
Abstract
The great majority of languages in the world are considered under-resourced for successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in low-resource languages and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation to previously unseen languages; and multilingual joint-training when limited target-language data is available during trai-ing. We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability, and show that meta-learning thrives in settings with a heterogeneous task distribution. We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state-of-the-art on a number of languages while performing on-par on others, using only a small amount of labeled data.- Anthology ID:
- 2021.eacl-main.168
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1966–1976
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.168
- DOI:
- 10.18653/v1/2021.eacl-main.168
- Cite (ACL):
- Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra, and Ekaterina Shutova. 2021. Multilingual and cross-lingual document classification: A meta-learning approach. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1966–1976, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual and cross-lingual document classification: A meta-learning approach (van der Heijden et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.eacl-main.168.pdf
- Code
- mrvoh/meta_learning_multilingual_doc_classification
- Data
- MLDoc