# Neural Machine Reading Comprehension: Methods and Trends
## 1 Introduction  
Machine reading comprehension (MRC) is a task introduced to test the degree to which a machine can understand natural languages by asking the machine to answer questions based on a given context, which has the potential to revolutionize the way in which humans and machines interact with each other. For example, as shown in Figure 1, a search engine with MRC techniques can directly return the correct answers to questions posed by users in natural language rather than a series of related web pages. In addition, smart assistants equipped with an MRC system can read help documents and provide users with high-quality consulting services. In sum, MRC is a promising task, which can make information retrieval more efficient. Early MRC systems date back to the 1970s, the most notable of which was the QUALM system proposed by [PERSON] [1]. However, restricted by its small scale and specific domain, this system could not be widely applied. Research on MRC was ignored in the 1980s and 1990s. In 1999, [PERSON] et al. [2] released an MRC dataset, containing stories of third- to sixth-grade material with five \"wh\" (what, where, when, why, and who) questions, which again directed the research attention to MRC tasks. At that time, methods for solving MRC tasks were mainly rule-based or machine-learning-based: [PERSON] et al. [2] proposed a bag-of-words technique that represented sentences with questions and context as sets of words and chose words occurring in both the question and context as the answer. [PERSON] et al. [3] designed a rule-based MRC system, Quarc, containing different heuristic rules with morphological analysis, such as part-of-speech tagging, semantic class tagging, and entity recognition, for different types of \"wh\" questions. [PERSON] et al. [4] used a machine-learning method combining bootstrapping, Markov logic, and self-supervised learning for machine reading. However,those methods suffer from some, if not all, of the following limitations. First, they are mainly based on hand-crafted rules or features that require substantial human effort. Second, those systems are incapable of generalization, and their performance may degrade due to large-scale datasets of myriad types of articles. Finally, some traditional approaches not only ignore long-range dependencies but also fail to extract contextual information. Due to the small size of human-generated datasets and the limitations of rule-based and machine-learning-based methods, early MRC systems did not perform well and hence could not be used in practical applications. This situation has changed since 2015, which can be attributed to two driving forces. On the one hand, MRC based on deep learning, also called neural machine reading comprehension, shows its superiority in capturing contextual information and dramatically outperforms traditional systems. On the other hand, a variety of large-scale benchmark datasets, such as CNN & Daily Mail [5], Stanford Question-Answering Dataset (SQuAD) [6] and MS MARCO [7], make it possible to solve MRC tasks with deep neural architectures and provide testbeds to extensively evaluate the performance of MRC systems. To illustrate the development trends of neural MRC more clearly, we conducted a statistical analysis of representative articles in this field, and the result is presented in Figure 2. As shown in this figure, on the whole, the number of articles increased exponentially from 2015 to the end of 2018. Furthermore, as time goes on, the types of MRC tasks are becoming increasingly diverse. All these demonstrate that neural MRC is under rapid development and has become the research focus of both academia (Stanford, Carnegie Mellon, etc.) and industry (Google, Facebook, Microsoft, etc.) demonstrated by a large number of authors coming from these institutions. The flourishing research of neural MRC calls for surveys to systemically study and analyze the recent successes. However, such a comprehensive review still cannot be found. Though [PERSON] et al.