A survey of deep learning techniques for machine reading comprehension

Reading comprehension involves the process of reading and understanding textual information in order to answer questions related to it. It finds practical applications in various domains such as domain-specific FAQs, search engines, and dialog systems. Resource-rich languages like English, Japanese, Chinese, and most European languages benefit from the availability of numerous datasets and resources, enabling the development of machine reading comprehension (MRC) systems. However, building MRC systems for low-resource languages (LRL) with limited datasets, such as Vietnamese, Urdu, Bengali, and Hindi, poses significant challenges. To address this issue, this study utilizes quantitative analysis to conduct a systematic literature review (SLR) with the aim of comprehending the recent global shift in MRC research from high-resource languages (HRL) to low-resource languages. Notably, existing literature reviews on MRC lack comprehensive studies that compare techniques specifically designed for rich and low-resource languages. Hence, this study provides a comprehensive overview of the MRC research landscape in low-resource languages, offering valuable insights and a list of suggestions to enhance LRL-MRC research.