Multi-granularity Hierarchical Feature Extraction for Question-Answering Understanding

Question-answering understanding systems are of central importance to many natural language processing tasks. A successful question-answering system first needs to accurately mine the semantics of the problem text and then match the semantic similarity between the question and the answer. Most of the current pre-training language modes use joint coding of questions and answers, a pre-training language model to avoid the problem of feature extraction from multilevel text structure, it through unified advance training ignores text semantic expression in different particle size, different levels of semantic features, and to some extent avoiding the serious problem of semantic understanding. In this paper, we focus on the problem of multi-granularity and multi-level feature expression of text semantics in question and answer understanding, and design a question-answering understanding method for multi-granularity hierarchical features. First, we extract features from two aspects, the traditional language model and the deep matching model, and then fuse these features to construct the similarity matrix, and learn the similarity matrix by designing three different models. Finally, the similarity matrix is learned by three different models, and after sorting, the overall similarity is obtained from the similarity of multiple granularity features. Evaluated by testing on WikiQA public datasets, experiments show that the results of our method are improved by adding the multi-granularity hierarchical feature learning method compared with traditional deep learning methods.