# Improving code-mixed hate detection by native sample mixing: A case study for Hindi-English code-mixed scenario

arXiv: https://arxiv.org/abs/2405.20755 · HTML: https://mlsanity.com/paper/2405.20755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [Exploring Hate Speech Detection in Multimodal Publications](https://mlsanity.com/paper/1910.03814/) (2019)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation](https://mlsanity.com/paper/2109.04588/) (2021)
- [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022)
- [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://mlsanity.com/paper/2205.00328/) (2022)
- [A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research](https://mlsanity.com/paper/1802.09416/) (2018)
- [BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Text](https://mlsanity.com/paper/2311.00116/) (2023)
- [Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech](https://mlsanity.com/paper/2301.11579/) (2023)
- [Stacked DeBERT: All Attention in Incomplete Data for Text Classification](https://mlsanity.com/paper/2001.00137/) (2020)

## Built on by (most cited first, 2 of 2)

- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Neither Here Nor There: Cross-Lingual Representation Dynamics of Code-Mixed Text in Multilingual Encoders](https://mlsanity.com/paper/2603.19771/) (2026)

## Abstract (arXiv)

Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the hate expressed through language alteration. Compared to the monolingual setup, we see much less work on code-mixed hate as large-scale annotated hate corpora are unavailable for the study. To overcome this bottleneck, we propose using native language hate samples (native language samples/ native samples hereafter). We hypothesise that in the era of multilingual language models (MLMs), hate in code-mixed settings can be detected by majorly relying on the native language samples. Even though the NLP literature reports the effectiveness of MLMs on hate detection in many cross-lingual settings, their extensive evaluation in a code-mixed scenario is yet to be done. This paper attempts to fill this gap through rigorous empirical experiments. We considered the Hindi-English code-mixed setup as a case study as we have the linguistic expertise for the same. Some of the interesting observations we got are: (i) adding native hate samples in the code-mixed training set, even in small quantity, improved the performance of MLMs for code-mixed hate detection, (ii) MLMs trained with native samples alone observed to be detecting code-mixed hate to a large extent, (iii) the visualisation of attention scores revealed that, when native samples were included in training, MLMs could better focus on the hate emitting words in the code-mixed context, and (iv) finally, when hate is subjective or sarcastic, naively mixing native samples doesn't help much to detect code-mixed hate. We will release the data and code repository to reproduce the reported results.