A Review on Textual Question Answering with Information Retrieval and Deep Learning Aspect

Text-based Question Answering (QA) is an essential task in natural language processing that aims to provide relevant and accurate answers to users’ queries. Traditional approaches to QA relied on rule-based systems and hand-crafted features. However, recent advancements in information retrieval and deep learning have shown promising results in improving the performance of QA systems. The combination of IR and deep learning techniques has led to significant improvements in QA performance. Hybrid models, such as the bi-encoder and tri-encoder architectures, have been proposed to leverage the strengths of both IR and deep learning models. Additionally, pre-training techniques, such as BERT and RoBERTa, have been shown to improve the performance of QA systems by providing pre-trained contextualized word embeddings. This study provides a brief overview of the QA system and its different domains and subdomains. This study also provides review of different proposed models and different datasets available for the task and evaluate their performance using some performance metrics. A comparison between various techniques are employed using the obtained result.