Review of Conversational Machine Reading Comprehension

Abstract

Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC.

Keywords

• multi-turn conversation
• machine reading comprehension (mrc)
• conversational question answering (convqa)