Abstract
Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models’ performance significantly for low-resource languages. Our code and trained models are available at: https://github.com/CALEDIPQALL/- Anthology ID:
- 2021.icon-main.66
- Volume:
- Proceedings of the 18th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2021
- Address:
- National Institute of Technology Silchar, Silchar, India
- Editors:
- Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 544–549
- Language:
- URL:
- https://aclanthology.org/2021.icon-main.66
- DOI:
- Cite (ACL):
- Hariom Pandya, Bhavik Ardeshna, and Brijesh Bhatt. 2021. Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 544–549, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
- Cite (Informal):
- Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages (Pandya et al., ICON 2021)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2021.icon-main.66.pdf
- Code
- Bhavik-Ardeshna/Question-Answering-for-Low-Resource-Languages
- Data
- MLQA, XQuAD