Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts
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Abstract (arXiv)
Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Polit'ecnico Nacional, Centro de Investigaci'on en Computaci'on (CIC) team's system description paper for the CoLI-Kanglish shared task at ICON2022. In this paper, we propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 22 references that are in the map. First posted on arXiv 26 Nov 2022.
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7 of 22 references.
References · 7
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2020Word Level Language Identification in English Telugu Code Mixed Datacited by 7
- 2021Language Identification of Hindi-English tweets using code-mixed BERTcited by 4
- 2022CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Textscited by 2
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