Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages

Hariom Pandya, Bhavik Ardeshna, Brijesh Bhatt

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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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/2021.icon-main.66.pdf
Optional supplementary material:
 2021.icon-main.66.OptionalSupplementaryMaterial.pdf
Code
 Bhavik-Ardeshna/Question-Answering-for-Low-Resource-Languages
Data
MLQA, XQuAD
PDF Search Code Optional supplementary material