Text semantic matching algorithm based on the introduction of external knowledge under contrastive learning

Measuring the semantic similarity between two texts is a fundamental aspect of text semantic matching. Each word in the texts holds a weighted meaning, and it is essential for the model to effectively capture the most crucial knowledge. However, current text matching methods based on BERT have limitations in acquiring professional domain knowledge. BERT requires extensive domain-specific training data to perform well in specialized fields such as medicine, where obtaining labeled data is challenging. In addition, current text matching models that inject domain knowledge often rely on creating new training tasks to fine-tune the model, which is time-consuming. Although existing works have directly injected domain knowledge into BERT through similarity matrices, they struggle to handle the challenge of small sample sizes in professional fields. Contrastive learning trains a representation learning model by generating instances that exhibit either similarity or dissimilarity, so that a more general representation can be learned with a small number of samples. In this paper, we propose to directly integrate the word similarity matrix into BERT’s multi-head attention mechanism under a contrastive learning framework to align similar words during training. Furthermore, in the context of Chinese medical applications, we propose an entity MASK approach to enhance the understanding of medical terms by pre-trained models. The proposed method helps BERT acquire domain knowledge to better learn text representations in professional fields. Extensive experimental results have shown that the algorithm significantly improves the performance of the text matching model, especially when training data is limited.