Short text matching model with multiway semantic interaction based on multi-granularity semantic embedding

Short text matching is a fundamental technique of natural language processing. It plays an important role in information retrieval, question answering and paraphrase identification, etc. However, due to the lack of available data after Chinese short text word segmentation, we need to take full advantage of the existing text information. In our paper, we propose a sentence matching model with multiway semantic interaction based on multi-granularity semantic embedding(MSIM) to dispose of the problem of Chinese short text matching. First, each sentence pair is represented as multi-granularity embedding: character embedding based on one hot vector, and word embedding obtained from the pre-trained model. In addition, we add the attention mechanism after the character embedding to weight the characters. In order to capture sufficient semantic features, we process short sentence pairs in three ways. We not only match each time step of the two encoded sentences and perform average pooling and maximum pooling operations, but also make deep interaction between each time step representation with attention representation. Finally, we employ BiLSTM to aggregate matching results into a fixed-length matching vector, with the decision made through a fully connected layer. Our method is evaluated on the Chinese datasets CCKS and ATEC. Experimental results demonstrate that the method in our paper takes full advantage of Chinese short text information, outperforming other methods.