On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish

Performance of downstream NLP tasks on code-switched Hindi-English (aka Hinglish ) 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 Hinglish code-switching benchmark GLUECoS and report significant improvements.