# Towards Code-switched Classification Exploiting Constituent Language Resources

arXiv: https://arxiv.org/abs/2011.01913 · HTML: https://mlsanity.com/paper/2011.01913/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching](https://mlsanity.com/paper/2401.16895/) (2024)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Adapting Multilingual Models to Code-Mixed Tasks via Model Merging](https://mlsanity.com/paper/2510.19782/) (2025)
- [Detecting Anchors' Opinion in Hinghlish News Delivery](https://mlsanity.com/paper/2204.02155/) (2022)
- [Neither Here Nor There: Cross-Lingual Representation Dynamics of Code-Mixed Text in Multilingual Encoders](https://mlsanity.com/paper/2603.19771/) (2026)

## Abstract (arXiv)

Code-switching is a commonly observed communicative phenomenon denoting a shift from one language to another within the same speech exchange. The analysis of code-switched data often becomes an assiduous task, owing to the limited availability of data. We propose converting code-switched data into its constituent high resource languages for exploiting both monolingual and cross-lingual settings in this work. This conversion allows us to utilize the higher resource availability for its constituent languages for multiple downstream tasks. We perform experiments for two downstream tasks, sarcasm detection and hate speech detection, in the English-Hindi code-switched setting. These experiments show an increase in 22% and 42.5% in F1-score for sarcasm detection and hate speech detection, respectively, compared to the state-of-the-art.