LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching
This work improves text matching for Chinese NLP applications, but it is incremental as it builds on existing methods with external knowledge.
The paper tackled Chinese short text matching by addressing polysemy and word segmentation issues, resulting in a model that outperformed existing approaches on two datasets.
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.