## 1 Introduction  
Reading comprehension plays an important role in people's daily life, and it is usually the core method for people to obtain information in many scenarios such as daily conversations, examinations, and information searches. With the development of technology and the requirements of intelligent applications, Machine Reading Comprehension (MRC) has become an important topic in the direction of Natural Language Processing (NLP), which is of great significance for guiding machines to understand human languages, improving the intelligence level of machines and enabling machines to acquire knowledge continuously [1]. MRC can help users find the most relevant information from a large number of complex texts by analyzing the given text and questions, guiding the machine to answer the questions around the text, and returning the correct answer [2]. At the same time, it is widely used because of its closeness to daily life. In recent years, there have been many improved models based on [PERSON] [3], such as ALBERT [4], RoBERTa [5], etc. The use of pre-trained models has become a development trend of MRC. Because the pre-trained model can only learn the shallow semantic matching information of the context, most of the current models adopt the combination of the pre-trained language model and the attention mechanism [6], that is, they obtain the corresponding representation through the pre-trained model, and then use the attention mechanism for reasoning, so as to capture the deep semantic information of the context and predict more accurate answers. However, the original pre-trained models are designed for the English applications, and Chinese has different grammatical structures and characteristics from English [7], so it cannot effectively process Chinese text. For example, \"[PERSON] Pai Mai [PERSON]\" has completely different semantics when the particles are \"[PERSON] Pai/ Mai/ [PERSON]\" (The badminton racket is worth 10 RMB) and \"[PERSON]/Pai/Mai/[PERSON]\" (The badminton auctioned for 10 RMB), so even the model with excellent performance will perform poorly when applied to Chinese text. In addition, in Chinese reading comprehension, the attention mechanism in the model is very important to obtain the correct answer. The existing attention mechanism usually only focuses on specific words, and it is difficult to mine the overall semantics of the context, which is poor for complex problem recognition. At the same time, the premise of MRC assumes that there are snippets in the article that match the question, but in reality, this is sometimes not the case, as shown in Figure 1. It has become an urgent task to understand the content of the passage correctly and to make a correct judgment on the answer ability of the questions. To address the above problems and to better accomplish the machine reading comprehension task, this paper proposes a Chinese machine reading comprehension algorithm, the AT-CRF Reader. This model introduces a pre-trained language model to fully acquire the underlying semantic features, combines CNN, attention mechanism, and position encoding to learn dependency information over long-distance. It also uses self-attention mechanism to obtain the internal representation of the context, and introduces Conditional Random Fields (CRF) [8] as the output layer to deal with unanswerable questions. Finally, the experimental results on two reading comprehension datasets, CMRC2018 [9] and DuReader-checklist [10], verify the effectiveness of the model in this paper. Figure 1: An example of the DuReader-checklist dataset. The same color in the article and the questions indicate that the corresponding content has relevant information; question 1 can find the corresponding answer fragment in the text, and question 2 has a similar correspondence in the text but is not the correct answer.