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2023 EMNLP EMNLP 2023

LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language

Abstract

AbstractThis paper describes the system of the LowResource Team for Task 2 of BLP-2023, which involves conducting sentiment analysis on a dataset composed of public posts and comments from diverse social media platforms. Our primary aim was to utilize BanglaBert, a BERT model pre-trained on a large Bangla corpus, using various strategies including fine-tuning, dropping random tokens, and using several external datasets. Our final model is an ensemble of the three best BanglaBert variations. Our system achieved overall 3rd in the Test Set among 30 participating teams with a score of 0.718. Additionally, we discuss the promising systems that didn’t perform well namely task-adaptive pertaining and paraphrasing using BanglaT5. Our training codes are publicly available at https://github.com/Aunabil4602/bnlp-workshop-task2-2023

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Aunabil Chakma , Masum Hasan

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Learning Types > Weakly Supervised Learning Deep Learning > Architectures > Transformers Natural Language Processing > Understanding > Sentiment Analysis Machine Learning > Learning Types > Transfer Learning Natural Language Processing > Applications > Sentiment Analysis

Keywords

transfer learning sentiment analysis text classification low-resource language language model model ensemble bert fine-tuning token dropping
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