# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 5 Experimentation
### Evaluation Criteria  
In this study, BLEU-4 [13] and ROUGE-L [17] metrics were simultaneously computed to evaluate the model performance:  
\[\text{BLEU}_{\text{n}}=\text{BP}\cdot\left(\prod_{i=1}^{n}\text{P}_{\text{i}} \right)^{\frac{1}{n}}. \tag{13}\]  
In terms of length statistics, the average number of sentences in the BLEU-4 dataset is 15.86, with an average of 7.26 questions per article, an average question length of 13.02 words, and an average answer length of 9.86 words. In addition, the ROUGE-L dataset was also collected, with an average number of paragraph sentences of 9.93 and an average paragraph length of 435.8 words. The average number of questions corresponding to each article was 3.44, with an average question length of 21.07 words and an average answer length of 4.86 words. And the DuReader2 dataset constructed in this article has an average paragraph sentence count of 32.93, with an average number of questions corresponding to each article of 11.47. The average question length and answer length are consistent with the DRCD dataset. According to the statistical results, it can be found that the paragraph length in the BLEU-4 dataset and DuReader dataset is much longer than that in the benchmark dataset ROUGE-L.  
BLEU\({}_{\text{n}}\) is the general calculating formula for N-gram scoring and is the evaluation index adopted in this chapter when the value is four, as shown in Formula (13). The scoring principle of the BLEU\({}_{\text{n}}\) calculation emphasizes the accuracy of the answer and reflects the fluency level of a text. BLEU ranges between 0 and 1 and is equal to the penalty factor BP multiplied by the N-gram precision of the candidate answers. P\({}_{\text{n}}\) is the ratio of the number of N-grams of the candidate answers matching the standard answer to the total number of candidate answers, as displayed in Formula (14). \[\text{P}_{\text{n}}=\frac{\text{c}(\text{matched ngram})}{\text{c}(\text{ candidate ngram})}. \tag{14}\]  
In contrast, the length penalty takes the length of the reference answer as a reference value for the penalty and penalizes candidate answers that are longer than the reference answer, as shown in Equation (15):  
\[\text{BP}=\text{e}^{\min(1-\tau/\text{c},0)} \tag{15}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistical Item** & **Article** & **Question** & **Answer** \\ \hline Number & 1,431,429 & 301,574 & 665,723 \\ The Average Length & 1793 (char) & 26 (char) & 299 (char) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the DuReader2 dataset. In the calculation of BP for multiple reference answers, the value of \(r\) is determined by the length of the longest reference answer, where \(r\) represents the maximum length. The value of \(c\) represents the length of the candidate answer, and \(min\) refers to the minimum value. This evaluation metric is known as BLEU-4. \[\text{ROUGE-L}=\frac{\left(1+\gamma^{2}\right)\text{Recall}_{\text{LCS}}\text{ Precision}_{\text{LCS}}}{\text{Recall}_{\text{LCS}}+\gamma^{2}\text{Precision}_{\text{LCS}}}. \tag{16}\]  
The ROUGE-L score is used to assess the precision and recall of the candidate answer and the reference answer based on their longest common subsequence. Recall indicates the recall rate of the longest common subsequence in the reference answer and is defined as the ratio of the length of the longest common subsequence to the length of the reference answer. Precision quantifies the accuracy rate of the longest common subsequence in the candidate answer and is defined as the ratio of the length of the longest common subsequence to the length of the candidate answer.