LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language
This work addresses sentiment analysis for low-resource Bangla language users, but it is incremental as it applies existing methods to a specific task.
The paper tackled sentiment analysis on Bangla social media data by leveraging BanglaBert with strategies like fine-tuning and ensembling, achieving 3rd place out of 30 teams with a score of 0.718.
This 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 is 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 has 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. Training codes and external datasets which are used for our system are publicly available at https://github.com/Aunabil4602/bnlp-workshop-task2-2023