Short Text Semantic Matching Strategy Fusing Sememe Similarity Matrix and Dual-channel of Char-Word Vectors

The purpose of the short text semantic matching task is to judge whether the semantics of two short text sentences are consistent.However,many existing methods often have shortcomings such as insufficient semantic information of short text and inability to effectively identify synonyms.In response to these shortcomings,this paper proposes a short text semantic matching strategy that fuses sememe similarity matrix and dual-channel of char-word vectors.Firstly,the pre-trained model Bert is used to encode the input sentence pairs;for the word-level semantic information in the sentence,the FastText model is used to train and obtain the word vector of the text,and the BiLSTM model is added to further extract the contextual semantic information.Se-condly,making effective use of the semantic information,multi-head attention and co-attention for interactive calculation of separation vectors are added to the above-mentioned dual-channel.And the semantic similarity matrix is integrated into the attentions respectively.Finally,infer the semantic consistency according to the above vectors.The effectiveness of the above algorithm is proved by experiments on the financial dataset BQ and the open domain dataset LCQMC.