Leveraging Multilingual Transformers for Hate Speech Detection

TL;DR

This paper demonstrates how multilingual Transformer models can effectively detect and classify hate speech, offensive, and profane content across multiple languages, achieving high accuracy on social media data.

Contribution

It introduces a multilingual Transformer-based approach for hate speech detection and classification, incorporating API features and feature selection analysis.

Findings

Achieved macro F1 scores of 90.29, 81.87, 75.40 for hate speech detection in English, German, Hindi.

Achieved macro F1 scores of 60.70, 53.28, 49.74 for fine-grained classification.

Showed the effectiveness of Perspective API features and multilingual training schemes.

Abstract

Detecting and classifying instances of hate in social media text has been a problem of interest in Natural Language Processing in the recent years. Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment on social media involves careful evaluation of the language style, semantic content and additional pointers such as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitter post is hateful and offensive or not. We further discriminate the detected toxic content into one of the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN) and (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages. On the provided testing corpora, we achieve Macro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi respectively while performing hate speech detection and of 60.70, 53.28 and 49.74 during fine-grained classification. In our experiments, we show the efficacy of Perspective API features for hate speech classification and the effects of exploiting a multilingual training scheme. A feature selection study is provided to illustrate impacts of specific features upon the architecture's classification head.