@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://preview.aclanthology.org/fix-sig-urls/2022.findings-emnlp.283/",
    doi = "10.18653/v1/2022.findings-emnlp.283",
    pages = "3859--3865",
    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."
}