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2022 EMNLP EMNLP 2022

On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish

Abstract

AbstractPerformance 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Vishwajeet Kumar , Rudra Murthy , Tejas Dhamecha

Topics

Artificial Intelligence > Learning Paradigms > Meta-Learning Natural Language Processing > Resources & Methods > Multilingual NLP Interdisciplinary > Linguistics > Computational Linguistics Machine Learning > Learning Paradigms > Meta-Learning

Keywords

multilingual nlp cross-lingual transfer low-resource language meta-learning framework
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