In recent years, short Text Matching tasks have been widely applied in the
fields ofadvertising search and recommendation. The difficulty lies in the lack
of semantic information and word ambiguity caused by the short length of the
text. Previous works have introduced complement sentences or knowledge bases to
provide additional feature information. However, these methods have not fully
interacted between the original sentence and the complement sentence, and have
not considered the noise issue that may arise from the introduction of external
knowledge bases. Therefore, this paper proposes a short Text Matching model
that combines contrastive learning and external knowledge. The model uses a
generative model to generate corresponding complement sentences and uses the
contrastive learning method to guide the model to obtain more semantically
meaningful encoding of the original sentence. In addition, to avoid noise, we
use keywords as the main semantics of the original sentence to retrieve
corresponding knowledge words in the knowledge base, and construct a knowledge
graph. The graph encoding model is used to integrate the knowledge base
information into the model. Our designed model achieves state-of-the-art
performance on two publicly available Chinese Text Matching datasets,
demonstrating the effectiveness of our model.Comment: 11 pages,2 figure