Multi-Knowledge Base Common Sense Question Answering Model Based on Local Feature Fusion

The input and feature combination of the current commonsense reasoning model based on multi-knowledge base fusion is too simple, resulting in the loss of some important information related to questions and answers, which limits the effect of the commonsense reasoning model integrating external knowledge. In addition, during the commonsense question and answer task, the problem of vector anisotropy in the output of the pre-training language model and the answer representation has not been solved. These problems are the factors that lead to the poor reasoning performance of commonsense question answering. To solve the above problems, this paper proposes a multi-knowledge base commonsense question answering model based on local feature fusion, which improves the fusion of external knowledge bases and question-answer texts. The model integrates the local question and answer features into the global features of the pre-trained language model to enrich the feature information of the model, and combines the features of multiple dimensions in the prediction layer for prediction. The model for the questions and answers to be matched. Sentence representations are whitened and then the matching task is performed. Through the whitening operation, the model enhances the isotropy of the sentence representation and improves the representation ability of the sentence vector. This paper also explores the effect of different pre-trained encoders (such as, ALBERT, ELECTRA) on the model to strengthen knowledge. The feature extraction ability of text is strengtened, and the stability of the model is proved. The experimental results show that under the same BERT-base encoder experiment, the accuracy of the model reaches 78.6%, which is 3.5 percentage points higher than the baseline model. In the experiment of ELECTRA-base encoder, the accuracy reaches 80.1%. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.