# WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments

arXiv: https://arxiv.org/abs/2011.00559 · HTML: https://mlsanity.com/paper/2011.00559/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans](https://mlsanity.com/paper/2104.04630/) (2021)
- [Transformers to Fight the COVID-19 Infodemic](https://mlsanity.com/paper/2104.12201/) (2021)
- [Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts](https://mlsanity.com/paper/2108.10939/) (2021)
- [Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers](https://mlsanity.com/paper/2312.05671/) (2023)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)
- [Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets](https://mlsanity.com/paper/2112.09986/) (2021)

## Abstract (arXiv)

This paper describes the WLV-RIT entry to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task 2020. The HASOC 2020 organizers provided participants with annotated datasets containing social media posts of code-mixed in Dravidian languages (Malayalam-English and Tamil-English). We participated in task 1: Offensive comment identification in Code-mixed Malayalam Youtube comments. In our methodology, we take advantage of available English data by applying cross-lingual contextual word embeddings and transfer learning to make predictions to Malayalam data. We further improve the results using various fine tuning strategies. Our system achieved 0.89 weighted average F1 score for the test set and it ranked 5th place out of 12 participants.