Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review

TL;DR

This paper reviews recent progress and ongoing challenges in developing Bangla question answering systems, highlighting innovative methods and the need for better datasets to improve language understanding capabilities.

Contribution

It provides a comprehensive overview of recent research efforts, innovative techniques, and persistent challenges in Bangla QA system development.

Findings

Introduction of LSTM-based models with attention mechanisms

Use of deep learning techniques based on prior knowledge

Identification of key challenges like data scarcity and understanding context

Abstract

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.