# Hate and Offensive Speech Detection in Hindi and Marathi arXiv: https://arxiv.org/abs/2110.12200 ยท HTML: https://mlsanity.com/paper/2110.12200/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 22 references that are in the map. - First posted on arXiv 23 Oct 2021. ## Builds on (most cited first, 9 of 9) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020) - [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021) - [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021) - [Deep Learning for Hindi Text Classification: A Comparison](https://mlsanity.com/paper/2001.10340/) (2020) - [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021) - [Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models](https://mlsanity.com/paper/1811.05145/) (2018) - [Evaluation of Deep Learning Models for Hostility Detection in Hindi Text](https://mlsanity.com/paper/2101.04144/) (2021) - [Comparative Analysis of Machine Learning and Deep Learning Algorithms for Detection of Online Hate Speech](https://mlsanity.com/paper/2108.01063/) (2021) ## Built on by (most cited first, 18 of 18) - [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022) - [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022) - [L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models](https://mlsanity.com/paper/2203.13778/) (2022) - [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022) - [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) - [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022) - [Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines](https://mlsanity.com/paper/2306.14764/) (2023) - [Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition](https://mlsanity.com/paper/2203.12907/) (2022) - [Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi](https://mlsanity.com/paper/2211.10163/) (2022) - [Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection](https://mlsanity.com/paper/2210.04267/) (2022) - [A Review of Challenges in Machine Learning based Automated Hate Speech Detection](https://mlsanity.com/paper/2209.05294/) (2022) - [The Uli Dataset: An Exercise in Experience Led Annotation of oGBV](https://mlsanity.com/paper/2311.09086/) (2023) - [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024) - [LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs](https://mlsanity.com/paper/2412.17131/) (2024) - [Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages](https://mlsanity.com/paper/2310.02249/) (2023) - [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026) - [Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages](https://mlsanity.com/paper/2401.03677/) (2024) - [MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization](https://mlsanity.com/paper/2604.21370/) (2026) ## Abstract (arXiv) Sentiment analysis is the most basic NLP task to determine the polarity of text data. There has been a significant amount of work in the area of multilingual text as well. Still hate and offensive speech detection faces a challenge due to inadequate availability of data, especially for Indian languages like Hindi and Marathi. In this work, we consider hate and offensive speech detection in Hindi and Marathi texts. The problem is formulated as a text classification task using the state of the art deep learning approaches. We explore different deep learning architectures like CNN, LSTM, and variations of BERT like multilingual BERT, IndicBERT, and monolingual RoBERTa. The basic models based on CNN and LSTM are augmented with fast text word embeddings. We use the HASOC 2021 Hindi and Marathi hate speech datasets to compare these algorithms. The Marathi dataset consists of binary labels and the Hindi dataset consists of binary as well as more-fine grained labels. We show that the transformer-based models perform the best and even the basic models along with FastText embeddings give a competitive performance. Moreover, with normal hyper-parameter tuning, the basic models perform better than BERT-based models on the fine-grained Hindi dataset.