# Neural Machine Reading Comprehension: Methods and Trends
## 3 Deep-Learning-Based Methods
### Typical Deep-Learning Methods
#### (2) Option Selector  
To tackle the multiple-choice task, the model should select the correct answer from candidate options. The common way is to measure the similarity between attentive context representations and candidate answer representations and the most similar candidate is chosen as the correct answer. [PERSON] et al. [28] use CNNs to encode question-option tuples and relevant context sentences. Then they measure the correlation between them by cosine similarity, and the most relevant option is selected as the answer. [PERSON] et al. [29] introduce the information of options to contribute to extracting the interaction between the context and the question. In the answer prediction module, they use a bilinear function to score each option according to the attentive information; the one with the highest score is the predicted answer. In the convolutional spatial attention model, [PERSON] et al. [27] calculate similarities among question-aware candidate representations, context-aware representations, and self-attended question representations with dot product to fully extract correlations among context,question, and options. The various similarities are concatenated and then fed to CNNs with different kernel sizes. The outputs of the CNNs are regarded as feature vectors and fed to fully connected layers to calculate a score for each candidate. Finally, the correct answer is the one with highest score. (3) Span Extractor  
The span extraction task can be regarded as an extension of cloze tests, which requires extracting a subsequence from the context rather than a single word. As word predictor methods used in models for cloze tests can only extract one context token, they cannot be directly applied to the span extraction task. Also inspired by pointer networks [56], [PERSON] and [PERSON] [15] propose two models, the sequence model and the boundary model to overcome the shortcomings of word prediction approaches. Outputs of the sequence model are positions where answer tokens appear in the original context. The process of answer prediction is similar to the decoding of sequence-to-sequence models, selecting tokens with the highest probability successively until it stops generating answer tokens. Answers obtained by such methods are treated as a sequence of tokens from the input context, which might not be a consecutive span and cannot be ensured to be a subsequence of original context. The boundary model can handle the problem, which only predicts the start and end positions of the answer. The boundary model is much simpler and shows better performance on SQuAD. It has been widely used in other MRC models as a preferred alternative for subsequence extraction. Considering that there is more than one plausible answer span in the original context, but the boundary model might extract incorrect answers with local maxima, [PERSON] et al. [17] proposed a dynamic pointing decoder to select an answer span by multiple iterations. This method uses LSTM to estimate the start and end positions based on representations corresponding to the last state answer prediction. To compute start and end score of context tokens, [PERSON] et al. propose highway maxout networks (HMN) with maxout networks [57] and highway networks [58], which require different models according to various question types and context topics. (4) Answer Generator  
With the appearance of free answering tasks, answers are no longer limited to a subsequence of the original context; instead, they need to be synthesized from the context and the question. Specifically, the expression of answers may differ from the evidence snippet in the given context, or answers may be from multiple pieces of evidence in different passages. Answer forms of the free answering task have the fewest limits, but this task has high requirements for the answer prediction module. To deal with the challenge, some generation approaches have been introduced to generate flexible answers. S-NET, proposed by [PERSON] et al.