Identifying Nuances of Multi-Task Learning for Bengali and English Emotional Texts
Abstract: Multi-task learning (MTL), a powerful paradigm in the field of machine learning enables us to learn and handle multiple different tasks simultaneously. Numerous advantages and novel approaches of MTL inspire us to analyze how MTL performs for low-resource languages such as Bengali. This paper proposes a fusion-based multilingual MTL framework for sentiment and emotion classification in Bengali and English languages with the help of transformer-based multilingual BERT and MuRUL models. Our fusion-based best-performing MTL framework achieves a macro F1 score of 71.14 and 38.92 for sentiment and emotion classification in the Bengali language and 67.09 and 83.48 for sentiment and emotion classification in the English language.
Paper Type: Short
Research Area: Machine Learning for NLP
Research Area Keywords: multi-task learning
Contribution Types: NLP engineering experiment
Languages Studied: Bengali, English
Submission Number: 443
Loading