Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review
It addresses the problem of improving Bangla QA systems for NLP researchers and practitioners, but is incremental as it synthesizes existing work rather than introducing new methods.
This paper reviews seven research articles on Bangla question answering systems, highlighting progress in methods like LSTM-based models with attention mechanisms, but notes ongoing challenges such as lack of well-annotated data and difficulties in word meaning understanding.
The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. These research studies explore different aspects of creating question-answering systems for the Bangla language. They cover areas like collecting data, preparing it for analysis, designing models, conducting experiments, and interpreting results. The papers introduce innovative methods like using LSTM-based models with attention mechanisms, context-based QA systems, and deep learning techniques based on prior knowledge. However, despite the progress made, several challenges remain, including the lack of well-annotated data, the absence of high-quality reading comprehension datasets, and difficulties in understanding the meaning of words in context. Bangla QA models' precision and applicability are constrained by these challenges. This review emphasizes the significance of these research contributions by highlighting the developments achieved in creating Bangla QA systems as well as the ongoing effort required to get past roadblocks and improve the performance of these systems for actual language comprehension tasks.