HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model
  for online comments
Neeraj Kumar Singh Koyel Ghosh Joy Mahapatra Utpal Garain Apurbalal Senapati
Warning: This paper contains examples of the language that some people may
find offensive.
  Detecting and reducing hateful, abusive, offensive comments is a critical and
challenging task on social media. Moreover, few studies aim to mitigate the
intensity of hate speech. While studies have shown that context-level semantics
are crucial for detecting hateful comments, most of this research focuses on
English due to the ample datasets available. In contrast, low-resource
languages, like Indian languages, remain under-researched because of limited
datasets. Contrary to hate speech detection, hate intensity reduction remains
unexplored in high-resource and low-resource languages. In this paper, we
propose a novel end-to-end model, HCDIR, for Hate Context Detection, and Hate
Intensity Reduction in social media posts. First, we fine-tuned several
pre-trained language models to detect hateful comments to ascertain the
best-performing hateful comments detection model. Then, we identified the
contextual hateful words. Identification of such hateful words is justified
through the state-of-the-art explainable learning model, i.e., Integrated
Gradient (IG). Lastly, the Masked Language Modeling (MLM) model has been
employed to capture domain-specific nuances to reduce hate intensity. We masked
the 50\% hateful words of the comments identified as hateful and predicted the
alternative words for these masked terms to generate convincing sentences. An
optimal replacement for the original hate comments from the feasible sentences
is preferred. Extensive experiments have been conducted on several recent
datasets using automatic metric-based evaluation (BERTScore) and thorough human
evaluation. To enhance the faithfulness in human evaluation, we arranged a
group of three human annotators with varied expertise.