A Text Semantic Matching Model with Chinese Characters'Glyph,Pinyin and Sense-based Multi-knowledge and Label Embedding
Text semantic matching aims to identify semantic relationships between texts based on the given texts.The existing methods neglect the enhancement and utilization of potential semantic information other than Chinese characters in the encoder and do not consider the impact of label information.Therefore,this paper proposes a text semantic matching method with multi-knowledge and label embedding via language models.Firstly,the information encodeing layer is used to encode the multi-knowledge of Chinese characters glyph,pinyin and sense.Next,the in-formation integration layer is used to get the joint representation of multi-knowledge of Chinese characters'glyph,pinyin and sense.Then,the label embedding layer utilizes the encoded representationof classificationlabels andjoint representation of multi-knowledge to generate the representation of supervised labels.Further,the label prediction layer acquires enhanced joint representations from both the textual and label aspects,and obtains the ultimate pre-diction of semantic relationships.The experiment results on multiple widely used datasets show that the proposed method is effective and outperforms previous state-of-the-art models.
Chinese characters'glyph,pinyin,sense-based multi-knowledgelabel embeddingtext semantic matc-hing