School of Foreign Languages, Fuzhou University of International Studies and Trade, Fuzhou, Fujian 350202, China.
School of Art and Design, Fuzhou University of International Studies and Trade, Fuzhou, Fujian 350202, China.
Comput Intell Neurosci. 2022 May 26;2022:4866531. doi: 10.1155/2022/4866531. eCollection 2022.
Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model's ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers' composition correction.
基于现有的优化神经网络算法，本文引入了一种简单且计算效率高的自适应机制（自适应指数衰减率）。通过将自适应机制应用于 Adadelta 算法，可以看出 AEDR-Adadelta 可以动态和自适应地获取学习率。同时，通过提出自适应指数衰减率，可以减少超参数的数量和配置方法，并为不同的参数有效地获得不同的学习率。该模型基于编码器-解码器结构，采用双编码器结构。Transformer 编码器用于提取句子的上下文信息；Bi-GRU 编码器用于提取源句子的信息；门控结构用于解码器端。整合输入信息，每个部分都与不同的注意力机制匹配，从而提高模型提取和分析句子中相关特征的能力。为了准确捕捉英语文本中的连贯性特征，使用改进的子图匹配算法挖掘句子语义图中频繁出现的子图模式，用于模拟英语文本中独特的连贯性模式，然后分析英语文本的整体连贯性。根据句子语义图中不同子图模式的出现频率，对子图进行过滤，生成频繁子图集，并分别计算每个频繁子图的子图频率。通过提取句子语义图中频繁子图的分布特征和子图的语义值，定量分析英语文本的整体连贯性质量。根据实验结果，使用自适应机制的算法可以减少训练集和测试集的误差，在一定程度上提高分类精度，并且具有更快的收敛速度和更好的文本泛化能力。本文提出的英语文本语义连贯性诊断模型在各种任务中表现良好，对提高英语作文自动纠错系统具有良好的效果，并为英语教师的作文批改提供参考。
