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ACL Findings2026

Disentangling Codemixing in Chats: The NUS ABC Codemixed Corpus

Svetlana Churina, Akshat Gupta, Nur Insyirah Binte Imam Mujtahid, Kokil Jaidka

Abstract

Code-mixing involves the seamless integration of linguistic elements from multiple languages within a single discourse, reflecting natural multilingual communication patterns. Despite its prominence in informal interactions such as social media, chat messages and instantmessaging exchanges, there has been a lack of publicly available corpora that are authorlabeled and suitable for modeling human conversations and relationships. This study introduces the first labeled and general-purpose corpus for understanding code-mixing in context while maintaining rigorous privacy and ethical standards. It includes over 355,641 messages spanning various code-mixing patterns, with a primary focus on English, Mandarin, and other languages. We expect the Codemix Corpus to serve as a foundational dataset for research in computational linguistics, sociolinguistics, and NLP applications. Code and dataset sample can be found here.

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Builds on2

  1. Multilingual Large Language Models Are Not (Yet) Code-SwitchersRuochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, Genta Indra Winata et al.EMNLP 2023 · 19 citations
  2. In-context Mixing (ICM): Code-mixed Prompts for Multilingual LLMsBhavani Shankar, Preethi Jyothi, Pushpak BhattacharyyaACL 2024

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