Chinese Semantic Matching with Multi-granularity Alignment and Feature Fusion

Chinese semantic matching is a fundamental task in natural language processing, which is critical and yet challenging for a series of downstream tasks. Although recent work on text representation learning has shown its potential in improving the performance on semantic matching, relatively limited work has been done on exploring the relevant interactive information between two granularity of Chinese text, i.e., character and word. Existing methods usually focus on capturing the interactive features from single granularity, which lead to inefficient text representation. Also, they typically fail to consider the fusion of features from different granularity. As a result, they only achieve limited performance improvement. This paper proposes a novel Chinese semantic matching model based on multi-granularity alignment and feature fusion (MAFFo). To be specific, we first encode the texts from different granularity, which are further handled with soft-alignment attention mechanism to extract relevant interactive information between texts on different granularity. In addition, we devise a feature fusion structure to merge the features from different granularity to generate an ideal representation for the pair of input text sequences, followed by a sigmoid function to judge the semantic matching degree. Extensive experiments on the publicly available dataset BQ demonstrate that our model can effectively improve the performance of semantic matching task and achieve comparable performance with BERT-based methods.