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.

翻译：自然语言处理领域在孟加拉语问答系统的发展中取得了显著进展。本文对推动该领域进步的七篇研究论文进行了全面综述。这些研究探讨了构建孟加拉语问答系统的不同方面，涵盖数据收集、预处理、模型设计、实验实施以及结果阐释等领域。相关论文引入了多种创新方法，例如采用带有注意力机制的LSTM模型、基于上下文的问答系统，以及基于先验知识的深度学习技术。然而，尽管已取得进展，仍存在若干挑战，包括缺乏高质量标注数据、缺少优质的阅读理解数据集，以及语境中词义理解的困难。这些挑战制约了孟加拉语问答模型的精确度与实际适用性。本综述通过重点阐述孟加拉语问答系统构建已取得的进展，以及突破障碍、提升系统在实际语言理解任务中性能所需的持续努力，强调了这些研究成果的重要意义。