  Chinese short text matching is a fundamental task in natural language
processing. Existing approaches usually take Chinese characters or words as
input tokens. They have two limitations: 1) Some Chinese words are polysemous,
and semantic information is not fully utilized. 2) Some models suffer potential
issues caused by word segmentation. Here we introduce HowNet as an external
knowledge base and propose a Linguistic knowledge Enhanced graph Transformer
(LET) to deal with word ambiguity. Additionally, we adopt the word lattice
graph as input to maintain multi-granularity information. Our model is also
complementary to pre-trained language models. Experimental results on two
Chinese datasets show that our models outperform various typical text matching
approaches. Ablation study also indicates that both semantic information and
multi-granularity information are important for text matching modeling.
