# Neural Machine Reading Comprehension: Methods and Trends
## 5 New Trends
### Knowledge-Based Machine Reading Comprehension  
MRC requires answering questions with knowledge implicit in the given context. Datasets such as the MCTest choose passages from specific corpora (fiction stories, children's books, etc.) to avoid introducing external knowledge. However, human-generated questions are usually too simple when compared to questions in real-world application. In the process of human reading comprehension, we may use common sense when we cannot answer a question simply by knowing about the context. External knowledge is so significant that is believed to be the biggest gap between MRC and human reading comprehension. As a result, interest in introducing world knowledge to MRC has surged in the research community, and knowledge-based machine reading comprehension (KBMRC) has come into being. KBMRC differs from MRC mainly in terms of inputs. In MRC, the inputs are sequences of context and questions. However, besides that, additional related knowledge extracted from knowledge bases is necessary in KBMRC. KBMRC can be regarded as augmented MRC with external knowledge \(K\) and it can be formulated as shown in Table 9:  
There are some KBMRC datasets in which world knowledge is needed to answer some questions. MCScripts [80] is a dataset about human daily activities, such as eating in a restaurant and taking a bus, where answering some questions requires common sense knowledge beyond the given context. As shown in Table 10, the answer to _Why are trees important?_ cannot be found in the given context. However, it is common sense knowledge to us that trees are important because they _create oxygen_ and absorb carbon dioxide via photosynthesis not because _they are green_. \begin{table}
\begin{tabular}{l l} \hline \hline
**MCScripts** & \\ \hline \multirow{4}{*}{Context:} & Before you plant a tree, you must contact the utility company. They will come to your property and mark out utility lines. Without doing this, you may dig down and hit a line, which can be lethal! Once you know where to dig, select what type of tree you want. Take things into consideration such as how much sun it gets, what zone you are in, and how quickly you want it to grow. Dig a hole large enough for the tree and roots. Place the tree in the hole and then fill the hole back up with dirt\(\cdots\) \\ \hline Question: & Why are trees important? \\ \cline{1-1} Candidate Answers: & A. create \(\mathcal{O}_{2}\)B. because they are green \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Some Examples in KBMRC \begin{table}
\begin{tabular}{l} \hline \hline
**Knowledge-Based Machine Reading Comprehension** \\ \hline Given the context \(C\), question \(Q\) and external knowledge \(K\), the task requires predicting the correct answer \(A\) by learning the function \(\mathcal{F}\) such that \(A=\mathcal{F}(C,Q,K)\). \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Definition of knowledge-based machine reading comprehension. The key challenges in KBMRC are as follows:  
* Relevant External Knowledge Retrieval  
There are various kinds of knowledge stored in knowledge bases, and entities may be misleading sometimes because of polysemy, e.g., \"apple\" can refer to a fruit or a corporation. Extracting knowledge closely related to the context and the question determines the performance of knowledge-based answer prediction. * External Knowledge Integration  
In contrast to text in the context and questions, knowledge in an external knowledge base has its own unique structure. How to encode such knowledge and integrate it with representations of the context and questions remains an ongoing research challenge. Some researchers have tried to address the above challenges in KBMRC. To make the model take advantage of the external knowledge, [PERSON] et al. [81] propose a new task, rare entity prediction, which requires predicting missing name entities and is similar to cloze tests. However, name entities removed from the context cannot be predicted correctly based only on the original context.