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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.findings-emnlp.283.pdf