# Neural Machine Reading Comprehension: Methods and Trends
## 1 Introduction
[8] recently give a brief overview to illustrate how to use deep-learning methods to deal with MRC tasks by introducing several classic neural MRC models, they neither give specific definitions of different MRC tasks nor compare them in depth. Moreover, they do not discuss the new trends and open issues in this field. Motivated by the lack of published surveys, we conducted a thorough literature review of recent progress in neural MRC with the expectation that it would help researchers, in particular newcomers, to obtain a panoramic view of this field. To achieve that goal, we collected papers mainly using Google Scholar ([[http://scholar.google.com](http://scholar.google.com)]([http://scholar.google.com](http://scholar.google.com))) with keywords including machine reading comprehension, machine comprehension, reading comprehension, deep learning, and neural networks. We selected from the search results only papers published on related high-profile conferences such as  
Figure 1: An example of search engine _Bing_ ([[https://cn.bing.com/](https://cn.bing.com/)]([https://cn.bing.com/](https://cn.bing.com/))) with machine reading comprehension (MRC) techniques. ACL, EMNLP, NAACL, ICLR, AAAI, IJCAI and CoNLL, and restricted the time range to 2015-2018. In addition, arXiv ([[http://arxiv.org/](http://arxiv.org/)]([http://arxiv.org/](http://arxiv.org/))), which includes some of the latest pre-print articles, was used as a supplementary source. Based on more than 85 frequently cited papers collected, a possible organization of the various facets of MRC as a field is shown in Figure 3. Our article follows a similar structure. First, we group common MRC tasks into four types according to [PERSON]'s categorization in her PhD thesis [9]: _cloze tests, multiple choice, span extraction,_ and _free answering._ We further extend this taxonomy by giving a formal definition to each type, comparing these tasks in different dimensions (Section 2). Second, we present the general architecture of neural MRC systems, which consists of four modules: _embeddings_, _feature extraction_, _context-question interaction_ and _answer prediction_. The prevalent deep learning techniques used in each module are also detailed (Section 3). Third, some representative datasets and evaluation metrics are described according to different tasks (Section 4). Fourth, some new trends, such as _knowledge-based MRC_, _MRC with unanswerable questions_, _multi-passage MRC_ and _conversational MRC_, are revealed by figuring out their challenges and describing existing approaches and limitations (Section 5). Finally, several open issues are discussed with the hope of shedding light on possible future research directions (Section 6). Before proceeding on the next section, as there are lots of terms mentioned in this article, to improve the readability for newcomers, definitions of terms can be found in a glossary presented in Appendix A.  
Figure 3: Facets of machine reading comprehension reflected in the structure of this article. Figure 2: The number of research articles concerned with neural MRC in this survey. KBMRC: knowledge-based machine reading comprehension; CMRC: conversational machine reading comprehension.