Construction of a cross-language text matching model based on SACNN

Abstract

The rapid progress of Internet technology has accelerated the development of natural language processing technology. To address the current issue of poor adaptability and accuracy in cross-language text matching and translation, firstly, a multi-head attention mechanism and convolutional neural network are introduced. Moreover, a cross-language text matching model based on similarity-based attention convolutional neural network is constructed. Then, visual features are added to the Transformer model to build a real-time machine translation model based on the improved Transformer. The results showed that the accuracy of the proposed text matching model could reach 83.42% when the epoch was 4. The proposed model achieved accuracy rates of 78.96%, 77.55%, and 79.86% in the experiment of matching French, German, and Spanish with English, respectively, while the accuracy rates were 79.16%, 75.03%, and 76.54% in the experiment of matching English with three languages. In addition, as the training data size increased from 1 M to 3 M, the Bilingual Evaluation Understud score of the proposed translation model improved by 36.45%, demonstrating good scalability. In summary, the model constructed in the study not only has high accuracy and adaptability but also demonstrates significant advantages in scalability.