Evaluation of Deep Learning Models for Hostility Detection in Hindi Text

TL;DR

This paper evaluates deep learning models like CNN, LSTM, and BERT for detecting hostility in Hindi social media text, highlighting BERT's superior performance in a low-resource language context.

Contribution

It introduces and compares deep learning approaches for multi-label hostility detection in Hindi, utilizing pre-trained embeddings and transformer models, which is novel for this language.

Findings

BERT-based models outperform CNN and LSTM.

Pre-trained multilingual transformers perform well on Hindi hostility detection.

CNN and LSTM models are competitive with BERT.

Abstract

The social media platform is a convenient medium to express personal thoughts and share useful information. It is fast, concise, and has the ability to reach millions. It is an effective place to archive thoughts, share artistic content, receive feedback, promote products, etc. Despite having numerous advantages these platforms have given a boost to hostile posts. Hate speech and derogatory remarks are being posted for personal satisfaction or political gain. The hostile posts can have a bullying effect rendering the entire platform experience hostile. Therefore detection of hostile posts is important to maintain social media hygiene. The problem is more pronounced languages like Hindi which are low in resources. In this work, we present approaches for hostile text detection in the Hindi language. The proposed approaches are evaluated on the Constraint@AAAI 2021 Hindi hostility detection dataset. The dataset consists of hostile and non-hostile texts collected from social media platforms. The hostile posts are further segregated into overlapping classes of fake, offensive, hate, and defamation. We evaluate a host of deep learning approaches based on CNN, LSTM, and BERT for this multi-label classification problem. The pre-trained Hindi fast text word embeddings by IndicNLP and Facebook are used in conjunction with CNN and LSTM models. Two variations of pre-trained multilingual transformer language models mBERT and IndicBERT are used. We show that the performance of BERT based models is best. Moreover, CNN and LSTM models also perform competitively with BERT based models.