2021
NAACL
NAACL 2021
Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text
Abstract
AbstractCode-mixing is a frequent communication style among multilingual speakers where they mix words and phrases from two different languages in the same utterance of text or speech. Identifying and filtering code-mixed text is a challenging task due to its co-existence with monolingual and noisy text. Over the years, several code-mixing metrics have been extensively used to identify and validate code-mixed text quality. This paper demonstrates several inherent limitations of code-mixing metrics with examples from the already existing datasets that are popularly used across various experiments.
🧭
Keyword Pioneer
— multilingual text processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio