# Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi arXiv: https://arxiv.org/abs/2211.10163 ยท HTML: https://mlsanity.com/paper/2211.10163/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 66 references that are in the map. - First posted on arXiv 18 Nov 2022. ## 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) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019) - [InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training](https://mlsanity.com/paper/2007.07834/) (2020) - [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019) - [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016) - [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020) - [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021) - [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020) - [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022) - [Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages](https://mlsanity.com/paper/2108.05927/) (2021) - [FBERT: A Neural Transformer for Identifying Offensive Content](https://mlsanity.com/paper/2109.05074/) (2021) - [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021) - [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021) - [Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages](https://mlsanity.com/paper/2112.09301/) (2021) - [A Dictionary-based Approach to Racism Detection in Dutch Social Media](https://mlsanity.com/paper/1608.08738/) (2016) - [QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language](https://mlsanity.com/paper/2008.12448/) (2020) - [One to rule them all: Towards Joint Indic Language Hate Speech Detection](https://mlsanity.com/paper/2109.13711/) (2021) - [Fine-tuning of Pre-trained Transformers for Hate, Offensive, and Profane Content Detection in English and Marathi](https://mlsanity.com/paper/2110.12687/) (2021) - [HateMonitors: Language Agnostic Abuse Detection in Social Media](https://mlsanity.com/paper/1909.12642/) (2019) - [Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection](https://mlsanity.com/paper/2210.04267/) (2022) - [A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification](https://mlsanity.com/paper/2202.02511/) (2022) ## Built on by (most cited first, 5 of 5) - [Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers](https://mlsanity.com/paper/2312.05671/) (2023) - [A Twitter BERT Approach for Offensive Language Detection in Marathi](https://mlsanity.com/paper/2212.10039/) (2022) - [Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages](https://mlsanity.com/paper/2310.02249/) (2023) - [An Under-Explored Application for Explainable Multimodal Misogyny Detection in code-mixed Hindi-English](https://mlsanity.com/paper/2601.08457/) (2026) - [NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey](https://mlsanity.com/paper/2602.15866/) (2026) ## Abstract (arXiv) The widespread of offensive content online has become a reason for great concern in recent years, motivating researchers to develop robust systems capable of identifying such content automatically. With the goal of carrying out a fair evaluation of these systems, several international competitions have been organized, providing the community with important benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three subtracks for English, Hindi, and Marathi. In this paper, we report the results of the HASOC 2022 Marathi subtrack which provided participants with a dataset containing data from Twitter manually annotated using the popular OLID taxonomy. The Marathi track featured three additional subtracks, each corresponding to one level of the taxonomy: Task A - offensive content identification (offensive vs. non-offensive); Task B - categorization of offensive types (targeted vs. untargeted), and Task C - offensive target identification (individual vs. group vs. others). Overall, 59 runs were submitted by 10 teams. The best systems obtained an F1 of 0.9745 for Subtrack 3A, an F1 of 0.9207 for Subtrack 3B, and F1 of 0.9607 for Subtrack 3C. The best performing algorithms were a mixture of traditional and deep learning approaches.