Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text

2021/06/18 by Vivek Srivastava, Srivastava, Vivek, Mayank Singh +1 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Natural Language Processing Techniques #Text Readability and Simplification

paper · pdf · doi:10.48550/arxiv.2106.10123

openalex publication_date 2021/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Code-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.

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• Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models
• From Injection to Defense: Constructing Edit-Based Fingerprints for Large Language Models

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