Exploring Hate Speech Detection with HateXplain and BERT

TL;DR

This paper enhances hate speech detection by fine-tuning BERT on the HateXplain dataset, focusing on explainability and bias reduction through novel loss functions, attention strategies, and masking techniques.

Contribution

It introduces three key innovations: rationale-class loss weighting, the use of conservative and lenient attention, and masking target community words to reduce bias.

Findings

Improved explainability metrics achieved.

Bias in model predictions significantly reduced.

Model performance on hate speech detection tasks was enhanced.

Abstract

Hate Speech takes many forms to target communities with derogatory comments, and takes humanity a step back in societal progress. HateXplain is a recently published and first dataset to use annotated spans in the form of rationales, along with speech classification categories and targeted communities to make the classification more humanlike, explainable, accurate and less biased. We tune BERT to perform this task in the form of rationales and class prediction, and compare our performance on different metrics spanning across accuracy, explainability and bias. Our novelty is threefold. Firstly, we experiment with the amalgamated rationale class loss with different importance values. Secondly, we experiment extensively with the ground truth attention values for the rationales. With the introduction of conservative and lenient attentions, we compare performance of the model on HateXplain and test our hypothesis. Thirdly, in order to improve the unintended bias in our models, we use masking of the target community words and note the improvement in bias and explainability metrics. Overall, we are successful in achieving model explanability, bias removal and several incremental improvements on the original BERT implementation.