Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
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Abstract (arXiv)
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2020.
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Citing papers per year: 2021: 1; 2022: 6; 2023: 2; 2024: 1; 2025: 2.
12 papers.
- 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challengescited by 35
- 2022A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectivescited by 6
- 2021HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representationcited by 5
- 2023Persona-aware Generative Model for Code-mixed Languagecited by 3
- 2022A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformercited by 2
- 2024Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challengescited by 2
- 2022How Effective is Incongruity? Implications for Code-mix Sarcasm Detectioncited by 1
- 2022Automatic Identification of Motivation for Code-Switching in Speech Transcriptscited by 1
- 2022IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in Englishcited by 0
- 2023Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understandingcited by 0
- 2025Large Language Models for Toxic Language Detection in Low-Resource Balkan Languagescited by 0
- 2025Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Chartscited by 0
- 2025Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Charts27 Sep 2025
- 2025Large Language Models for Toxic Language Detection in Low-Resource Balkan Languages11 Jun 2025
- 2024Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges17 Jan 2024
- 2023Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding24 Sep 2023
- 2023Persona-aware Generative Model for Code-mixed Language6 Sep 2023
- 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges19 Dec 2022
- 2022Automatic Identification of Motivation for Code-Switching in Speech Transcripts30 Nov 2022
- 2022A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives10 Sep 2022
- 2022A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer27 Apr 2022
- 2022IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English4 Mar 2022
- 2022How Effective is Incongruity? Implications for Code-mix Sarcasm Detection6 Feb 2022
- 2021HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation30 May 2021