# Neural Machine Reading Comprehension: Methods and Trends
## 6 Open Issues  
Based on the analysis of the literature cited in this article, we observe that there are some open issues that remain unsolved in neural MRC, some of which may have been also discussed in related research, such as machine inference and open-domain QA. The most important issue in neural MRC is that the machine does not truly understand the given text, as existing models mainly rely on semantic matching to answer question. Experiments performed by [PERSON] and [PERSON] [105] show that some MRC models perform unexpectedly well when being provided with just passages or questions. Although researchers have already made great strides in neural MRC, there is still a giant gap between MRC and human comprehension in the following aspects:  
* Limitation of Given Context  
Similar to reading comprehension on language proficiency tests, machine reading comprehension asks the machine to answer questions based on the given context. Such context is a necessity in MRC tasks, but restricts the application. In the real world, machines are not expected to help students with their reading comprehension exams but make question-answering or dialogue systems smarter. Efforts made in multi-passage MRC research have somewhat broken the limitation of given context, but there is still a long way to go as how to find the most relevant resources for MRC systems effectively determines the performance of answer prediction. It calls for a deeper combination of information retrieval and machine reading comprehension in the future. * Robustness of MRC Systems  
As [PERSON] and [PERSON] [106] point out, most existing MRC models based on word overlap are weak to adversarial question-answer pairs. For SQuAD, they add distracting sentences to the given context, which have semantic overlap with the question and might cause confusion but do not contradict the correct answer. With such adversarial examples, the performance of MRC systems drops dramatically, which reflects that machines cannot really understand natural language. Although the introduction of answer verification components can alleviate the side-effect of plausible answers in a way, the robustness of MRC systems should be enhanced to face the challenges of adversarial circumstances. * Incorporation of External Knowledge  
As an essential component of human intelligence, accumulated common sense or background knowledge is usually used in human reading comprehension. To mimic this, a knowledge-based MRC is proposed to improve the performance of machine reading with external knowledge. However, how to effectively introduce external knowledge and make full use of it remains an ongoing challenge. On the one hand, the structure of knowledge stored in knowledge bases is so different from the text in context and questions that it is difficult to integrate them. On the other hand, the performance of knowledge-based MRC is closely related to the quality of the knowledge base. Constructing of a knowledge base is time-consuming, requiring considerable human efforts. In addition, knowledge in knowledge bases is sparse, and most of the time, relevant external knowledge cannot be found directly to support answer prediction and further reasoning is required. The effective fusion of knowledge graphs and machine reading comprehension needs to be further investigated. * Lack of Inference Ability  
As mentioned before, most existing MRC systems are mainly based on semantic matching between the context and the question to give the answer, which results in MRC models being incapable of reasoning. As an example given by [PERSON] et al. [107] shows, given the context that _five people on board and two people on the ground died_, the machine cannot infer the correct answer _seven_ to the question _how many people died_ because of the lack of inference ability. How to enable the machine with inference ability still requires further research.