Hate Speech and Toxic Comment Detection using Transformers

Authors

Pierre Guillaume, Corentin Duchene, Réda Dehak

Where

Workshop EGC 2022 DL for NLP

Type

inproceedings

Keywords

IA

Date

2022-01-12

Abstract

Hate speech and toxic comment detection on social media has proven to be an essential issue for content moderation. This paper displays a comparison between different Transformer models for Hate Speech detection such as Hate BERT, a BERT-based model, RoBERTa and BERTweet which is a RoBERTa based model. These Transformer models are tested on Jibes&Delight 2021 reddit dataset using the same training and testing conditions. Multiple approaches are detailed in this paper considering feature extraction and data augmentation. The paper concludes that our RoBERTa st4-aug model trained with data augmentation outperforms simple RoBERTa and HateBERT models.

Bibtex (lrde.bib)

@InProceedings{	  guillaume.22.egc,
  author	= {Pierre Guillaume and Corentin Duchene and Reda Dehak},
  title		= {Hate Speech and Toxic Comment Detection using
		  Transformers},
  booktitle	= {Workshop EGC 2022 DL for NLP},
  month		= jan,
  year		= {2022},
  abstract	= {Hate speech and toxic comment detection on social media
		  has proven to be an essential issue for content moderation.
		  This paper displays a comparison between different
		  Transformer models for Hate Speech detection such as Hate
		  BERT, a BERT-based model, RoBERTa and BERTweet which is a
		  RoBERTa based model. These Transformer models are tested on
		  Jibes&Delight 2021 reddit dataset using the same
		  training and testing conditions. Multiple approaches are
		  detailed in this paper considering feature extraction and
		  data augmentation. The paper concludes that our RoBERTa
		  st4-aug model trained with data augmentation outperforms
		  simple RoBERTa and HateBERT models.},
  category	= {national},
  note		= {accepted},
  nodoi		= {}
}