# Neural Machine Reading Comprehension: Methods and Trends
## 3 Deep-Learning-Based Methods
### Typical Deep-Learning Methods
#### 3.2.3 Context-Question Interaction
(4) Multi-Hop Interaction  
In contrast to one-hop interaction, multi-hop interaction is much more complex; it tries to mimic the rereading phenomenon of humans by computing the interaction between the context and the question more than once. In the process of the interaction, whether information of the previous state, i.e., the memory of the already read context and question, can be efficiently stored or not directly affects the performance of the next interaction. There are mainly three methods to perform multi-hop interaction:  
The first method calculates the similarity between the context and the question based on previous attentive representations of context. In the Impatient Reader model proposed by [PERSON] et al. [5], query-aware context representations are dynamically updated by this method as each query token is read. This simulates the process of humans rereading a given context with the question information. The second method introduces external memory slots to store previous memories. The representative model using this method is memory networks, proposed by [PERSON] et al. [54], which can explicitly store long-term memories and has easy access to reading memories. With such a mechanism, MRC models can have a deeper understanding of the context and the question by multiple turns of interaction. After being given the context as input, the memory mechanism stores information of the context in memory slots and then updates them dynamically. The process of answering involves finding the memory most relevant to the question and turning it into an answer representation as required. Although this method can overcome the shortcoming of insufficient memory, it is hard to train the network via back-propagation. To address this problem, [PERSON] et al. [11] introduce an end-to-end version of memory networks, in which explicit memory storage is embedded with continuous representations and the process of reading and updating memories is modeled by neural networks. This extension of memory networks can reduce supervision during training and is applicable to more tasks. The characteristic of updating memories with multiple hops in memory networks makes this method popular in MRC systems. [PERSON] et al. [20] propose the MEMEN model, which stores question-aware context representations, context-aware question representations, and candidate answer representations in memory slots and updates them dynamically. Similarly, [PERSON] et al. [55] use external memory slots to store question-aware context representations and update memories with bidirectional GRUs. The third method takes advantage of the recurrence feature of RNNs, using hidden states to store previous interaction information. [PERSON] and [PERSON] [15] perform multiple interaction by using match-LSTM architecture recurrently. This model is originally proposed for textual entailment, when introduced to MRC, it can simulate the process of reading passages with question information. First, [PERSON] and [PERSON] use the standard attention mechanism to obtain the attentive weight of each context token for the question. After calculating the dot product of question tokens and attentive weights, the model concatenates it with the context token and feeds it to a match-LSTM to get query-aware context representations. Similarly, this process is done in the reverse direction to fully encode contextual information. Finally, the outputs of match-LSTM in two directions are concatenated and fed to the answer prediction module. In addition, R-NET [21] and iterative alternating reader (IA Reader) [14] also use RNNs to update query-aware context representations to perform multi-hop interaction. Some early work treated each context and query word equally when mining their correlations. However, the most important part should be given more attention for efficient context-query interaction. The gate mechanism, which can control the amount of mutual information between the context and the question, is a key component in multi-hop interaction.