mlsanityData to 13 Oct 2026

Challenges and Considerations with Code-Mixed NLP for Multilingual Societies

arXiv 2106.07823 ↗First posted 15 Jun 2021cs.CL cs.LGcited by 4

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Abstract (arXiv)

Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech. This paper discusses the current state of the NLP research, limitations, and foreseeable pitfalls in addressing five real-world applications for social good crisis management, healthcare, political campaigning, fake news, and hate speech for multilingual societies. We also propose futuristic datasets, models, and tools that can significantly advance the current research in multilingual NLP applications for the societal good. As a representative example, we consider English-Hindi code-mixing but draw similar inferences for other language pairs

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 31 of its 121 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

31 of 121 references.

References · 31

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19 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  2. 2022L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Modelscited by 33
  3. 2022Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languagescited by 2
  4. 2023Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskritcited by 1