On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish

Vishwajeet Kumar, Rudra Murthy, Tejas Dhamecha


Abstract
Performance of downstream NLP tasks on code-switched Hindi-English (aka ) continues to remain a significant challenge. Intuitively, Hindi and English corpora should aid improve task performance on Hinglish. We show that meta-learning framework can effectively utilize the the labelled resources of the downstream tasks in the constituent languages. The proposed approach improves the performance on downstream tasks on code-switched language. We experiment with code-switching benchmark GLUECoS and report significant improvements.
Anthology ID:
2022.findings-emnlp.283
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3859–3865
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.283
DOI:
10.18653/v1/2022.findings-emnlp.283
Bibkey:
Cite (ACL):
Vishwajeet Kumar, Rudra Murthy, and Tejas Dhamecha. 2022. On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3859–3865, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish (Kumar et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.283.pdf