Semantic Matching for Chinese Language Approach Using Refined Contextual Features and Sentence–Subject Interaction

Aiming at the problems of noise interference and the lack of information on sentence–subject interactions in Chinese matching models, which lead to their low accuracy, a new semantic matching method using fine-grained context features and sentence–subject interaction is designed. Compared with the method that relies on noisy data to improve the noise resistance of the model, we introduce data pollution and imprecise noise processing. We also design a novel context refinement strategy, which uses dot product attention, a gradient reversal layer, the Softmax function, and projection theorem to accurately identify and eliminate noise features to obtain high-quality refined context features, effectively overcoming the above shortcomings. Then, we innovatively use a one-way interaction strategy based on the projection theorem to map the sentence subject to the refined context feature space, which produces effective interaction ability between the features in the model. The refined context and sentence’s main idea are fused in the final prediction stage to compute the matching result. In addition, this study uses Kullback–Leibler divergence scatter to optimize the distance between the distributions of the refined context and the sentence’s main idea so that their distributions are closer to each other. The experimental results show that the accuracy of the method on the PAWS dataset and F1 are 87.65% and 86.51%, respectively; 80.51% on the Ant Financial dataset, and 85.29% on the BQ dataset. The accuracies on the Medic-SM dataset and Macro-F1 are 74.69% and 74% respectively, both of which are superior to those of other methods.