Chinese Question Classification Based on ERNIE and Feature Fusion

Question classification (QC) is a basic task of question answering (QA) system. This task effectively narrows the range of candidate answers and improves the operating efficiency of the system by providing semantic restrictions for the subsequent steps of information retrieval and answer extraction. Due to the small number of words in the question, it is difficult to extract deep semantic information for the existing QC methods. In this work, we propose a QC method based on ERNIE and feature fusion. We approach this problem by first using ERNIE to generate word vectors, which we then use to input into the feature extraction model. Next, we propose to combine the hybrid neural network (CNN-BILSTM, which extracts features independently), highway network and DCU (Dilated Composition Units) module as the feature extraction model. Experimental results on Fudan university’s question classification data set and NLPCC(QA)-2018 data set show that our method can improve the accuracy, recall rate and F1 of the QC task.