EMNLP 2022finding0 citations
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.
BibTeX
@inproceedings{kumar-etal-2022-utilizing,
title = "On Utilizing Constituent Language Resources to Improve Downstream Tasks in {H}inglish",
author = "Kumar, Vishwajeet and
Murthy, Rudra and
Dhamecha, Tejas",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.283/",
doi = "10.18653/v1/2022.findings-emnlp.283",
pages = "3859--3865"
}