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HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech Detection

arXiv 2101.09007 ↗First posted 22 Jan 2021cs.CLcited by 2

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Abstract (arXiv)

Hateful and Toxic content has become a significant concern in today's world due to an exponential rise in social media. The increase in hate speech and harmful content motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. In this task, we propose an approach to automatically classify hate speech and offensive content. We have used the datasets obtained from FIRE 2019 and 2020 shared tasks. We perform experiments by taking advantage of transfer learning models. We observed that the pre-trained BERT model and the multilingual-BERT model gave the best results. The code is made publically available at https://github.com/suman101112/hasoc-fire-2020.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 22 Jan 2021.

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8 of 12 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2018Deep contextualized word representationscited by 2,285
  4. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  5. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  6. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  7. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  8. 2018Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approachcited by 10

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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.

2 papers.