# Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension
## 5 Results and Discussion
### Experiment Results  
We present a comparison between the proposed model and the baseline models on the SQuAD 2.0 and CMRC 2018 dev sets, including three traditional methods and several fine-tuned methods based on PrLMs. The main results are presented in Tables 1 and 2.  
The results indicate that the proposed model combined with the various pre-trained models mentioned indicate varying degrees of improvement, some of which are significant. For the SQuAD dataset, comparing the same pre-trained models but with different sizes. Although the improvement of the large model is not as large as that of the small model, there is still a relatively obvious improvement. For example, the EM of BERT-large has increased by 0.5%. Compared with the large improvement of the small model, because the learning ability of the large model is strong enough, some grammatical relations have been learned, which leads to the fact that the additional feature information we have may have been learned, so compared with the small model, the improvement is not very obvious. Then, the improvement for albert-base+MgSG is better than that of BERT-large,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**EM(\%)**} & \multirow{2}{*}{**F1(\%)**} & \multicolumn{2}{c}{**EM(\%) for**} \\  & & & **MgSG** & \\ \hline Human & & & & \\ performance & & & & \\ \hline QANet & 62.6 & 66.7 & & \\ SAN & 68.2 & 70.9 & & \\ Match-LSTM & 60.3 & 63.5 & & \\ \hline BERT-base & 75.8 & 79.2 & 77.2 & 81.1 \\ BERT-large & 80.4 & 83.3 & 80.9 & 84.0 \\ ALBERT-base & 77.1 & 80.1 & 81.5 & 84.8 \\ ALBERT-large & 79.4 & 82.3 & 81.8 & 85.6 \\ ALBERT-xlarge & 85.6 & 88.1 & 85.9 & 88.2 \\ XLNET-base & 77.6 & 80.3 & 79.5 & 82.1 \\   BERT-base & 76.3 & 80.1 & 76.9 & 80.5 \\   BERT-large & 82.8 & 86.1 & 82.5 & 86.0 \\ ELECTRA-base & 79.5 & 82.5 & 80.2 & 83.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for SQuAD 2.0 dev dataset. The results of the experiments are obtained in our local environment and the corresponding literatures.  
and the amount of parameters is much smaller than that of BERT-large. This proves that the improvement of our module is more obvious for small models, which provides a new idea for model Lightweighting.  
For the CMRC dataset, it can be seen from Table 2 that the fusion of multigranularity features with the baseline model also effects a performance improvement of approximately 1% over the baseline model. However, CMRC is a Chinese dataset. Different languages bring different grammatical rules and semantics. Compared with Chinese grammar rules, English grammar rules are obviously clearer, and the semantic information contained in the dependencies of words is also clearer, which can greatly help the understanding of sentences. Therefore, the improvement of the model for English data is greater. Due to the grammatical structure of Chinese is more random and irregular, which makes it difficult for us to use the information contained in Chinese grammar accurately and correctly. On the other hand, it proves that our model can make good use of the hidden information contained in explicit grammatical structures and the use of weak grammatical structures is less stable.