On Importance of Code-Mixed Embeddings for Hate Speech Identification
This addresses hate speech identification in multilingual communities like India, but it is incremental as it applies existing models to a specific code-mixed dataset.
The study tackled the challenge of hate speech detection in code-mixed Hindi-English text by evaluating BERT and HingBERT models, finding that HingBERT outperformed BERT and code-mixed Hing-FastText beat standard English FastText and vanilla BERT.
Code-mixing is the practice of using two or more languages in a single sentence, which often occurs in multilingual communities such as India where people commonly speak multiple languages. Classic NLP tools, trained on monolingual data, face challenges when dealing with code-mixed data. Extracting meaningful information from sentences containing multiple languages becomes difficult, particularly in tasks like hate speech detection, due to linguistic variation, cultural nuances, and data sparsity. To address this, we aim to analyze the significance of code-mixed embeddings and evaluate the performance of BERT and HingBERT models (trained on a Hindi-English corpus) in hate speech detection. Our study demonstrates that HingBERT models, benefiting from training on the extensive Hindi-English dataset L3Cube-HingCorpus, outperform BERT models when tested on hate speech text datasets. We also found that code-mixed Hing-FastText performs better than standard English FastText and vanilla BERT models.