# Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension
###### Abstract
[20] propose an approach based on semantic parsing to answer simple and complex questions and resolves ambiguity in natural language problems. Thus, a significant improvement has been achieved in syntactic parsing. Meanwhile, graph neural networks are beginning to be used for natural language task processing owing to their ability to model non-Euclidean spatial data. [PERSON] et al. [21] used a combination of dependent syntax and graph convolution neural networks to perform a sentiment analysis based on comments from Internet users. [PERSON] et al. [22] constructed parallel GCNNs and fused them with LSTM to extract graph domain features from feature cubes. Figure 1: Question–answer pairs of SQuAD2.0. In this dataset, except the answer is No answer, other answers can be found in the passage. [PERSON] et al. [23] modeled text hierarchically at multiple-granularity levels and used graph attention networks to obtain various granularity representations to model the dependencies between the different granularities. [PERSON] et al. [24] propose a novel Hierarchical-Cumulative Graph Convolutional Network (HC-GCN) to generating Social Relationship Graphs for Multiple Characters in Videos  
To make up for the inability of the MRC model to deal with the two shortcomings of long texts and intersentence semantics, such that the model can understand long texts and accurately discriminate the semantics just like humans, we propose a graph neural network to feature textual sentences and in-sentence entities. Based on the feature that the different levels of granularity contain different levels of semantic information, a vast variety of semantic information is integrated to make the model's understanding of the various semantics accurate. Thus, whether a question is answerable or not can be effectively distinguished based on the granularity of the sentence. The start and end positions, which are immensely difficult to identify, can be obtained following multigranularity fusion. Experiments conducted on the SQuAD indicate that multigranularity syntax guidance (MgSG) outperforms traditional models in terms of both exact match (EM) and F score(F1). The main contributions of this study are as follows:  
* A new network structure, MgSG, is proposed. Based on the use of PrLMs to represent the text, combined with the graph structure, word and sentence granularities are used to obtain a text representation with richer semantics. * Two graph structure construction methods are designed using dependencies and named entities, and a filtering mechanism is proposed to integrate them to improve the accuracy of the overall text representation. * The role of the dependencies and named entities in reading comprehension tasks is analyzed, and it is demonstrated through experiments that both word and sentence granularities affect model performance. In addition, the two granularity representations are modularized to make them compatible with more models. The proposed method is used for evaluation on the SQuAD and superior results are achieved in terms of both EM and F1 values when compared with the BERT-based reading comprehension model, demonstrating the effectiveness of the method. The rest of the paper is organized as follows. Related work is summarized in Section 2. The various components of the model proposed in the text are described in detail in Section 3. The experimental results and analyses are presented in Section 4. The analysis and discussion of the effectiveness of the proposed method based on experiments are presented in Section 5. The conclusions are presented in Section 6.