Bengali Multi-class Text Classification via Enhanced Contrastive Learning Techniques

Bengali, one of South Asia's most frequently spoken languages, poses substantial difficulties in tasks such as sentiment analysis and other forms of text classification due to its intricate grammatical structure. This is not just vital for protecting mental health through precise sentiment analysis, but it also has broader ramifications in sectors where accurately discriminating between fine-grained meanings is critical. Improving classification methods to address these subtle distinctions is a timely necessity for advancing natural language processing in Bengali. Our study aims to advance the field of Bengali text classification by implementing Token-level Adversarial Contrastive Training (TACT) and Label-aware Contrastive (LCL) loss, leveraging contrastive learning methods. The two new losses distinguished fine-grained text better, compared to our previous findings on Contrastive Adversarial Training (CAT) and Supervised Contrastive Loss (SCL). For binary class classification, TACT reached an F1-score of 98% outperforming CAT and LCL and setting a new benchmark on the Rokomari Book Review (RBR) dataset. For multi-class classification, TACT achieved an F1-score of 91%, matching the current benchmark on the Bengali Hate Speech (BHS-M) dataset. Furthermore, our custom Bengali multi-class text classification dataset, Daraz Product Review (DPR) further contributes to the field.