# A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
## 1 Introduction  
How to make computers understand human language is the main goal of the field of Natural Language Processing (NLP) and has been a long-standing challenge for artificial intelligence research. Machine Reading Comprehension (MRC) tasks are similar to human reading comprehension tests in which the computer needs to answer questions based on the content of a given text [1]. In contrast to traditional NLP, MRC requires techniques that involve multiple aspects of lexical, grammatical and syntactic meanings and also requires a combination of feature representations analysis of the text context and semantic reasoning techniques, making it a very challenging NLP task. In MRC tasks, deep learning models are often used to help machines learn and understand contextual content so that they can answer the corresponding questions correctly. If machines can perform reading comprehension tasks similarly to humans, and have reading comprehension capabilities similar to or better than those of the human brain, then they can be of great value in replacing traditional human reading comprehension tasks. Among the several types of tasks in MRC (cloze test, span extraction and multiple choice, etc. ), this paper focuses on multiple-choice style tasks. A multiple-choice MRC task differs from a span extraction task in that it requires not only the text and questions but also a set of candidate answers from which the machine needs to find the correct answer, taking into account the semantic information of the text [2]. In contrast to the cloze test MRC task where the answers are fixed words and phrases, the answers to the multiple-choice MRC task are artificially generated sentences that are manually rewritten with complete logic based on the content of the text. Typical English datasets of this type include MCTest [3], RACE [4] and MCScript [5], and a representative Chinese dataset is\(C^{3}\)[6]. A sample of data selected from the RACE dataset for a multiple-choice MRC task is shown in Figure 1. The RACE dataset is a representative benchmark dataset for multiple-choice reading comprehension, which is constructed using an English test bank for junior and senior high schools. The candidates in candidate answer set A in Figure 1 never appear in the passage, and the machine needs to fully understand the semantic information of the text context. The machine needs to fully understand the semantic information of the context and select semantically similar candidates from the candidate set as the answer. While the answers to the cloze test and span extraction tasks must come from the context of the given passage, and the answers to the multiple-choice tasks are not necessarily sequences in the text. Those answers are manually rewritten and summarized based on the content of the text, and some answers even need to be inferred together with external knowledge.Questions and answer candidates for multiple-choice Chinese MRC are written by humans, which means the content is more flexible and difficult to find out the correct answer by simple matching. Through our analysis of the multiple-choice Chinese MRC task, we found these three factors to make the task challenging: (1) few training data and lack of external knowledge severely limit the accuracy of the model; and (2) the answer selection for many questions requires deep semantic interaction to find out the corresponding answer. Repeated semantic interactions between articles, questions and candidates are crucial, but the learning of them is inadequate. (3) MRC requires a high level of semantic reasoning, and answer selection must not only take into account the local information of the related passages but also consider the global information of the article. Based on the characteristics and challenges mentioned above, we propose a deep neural network (DNN) based model. The main contributions of this paper can be summarized as follows:  
Figure 1: Example of multiple-choice MRC task data.