Verification of Role Classification Method for Proceedings of the National Diet Using BERT-Based Classifier

Abstract

In this paper, we test the effectiveness of a method for role-based classification of statements in the National Diet using a BERT-based classifier. Experimental results using two models, FP_MinWiki, which was pre-trained with the Local Meeting Minutes, the National Diet Record, and Wikipedia, and the Tohoku University Japanese BERT model, which was pre-trained with a wide range of Japanese data including Wikipedia, showed that the accuracy of the "opinion/non-opinion" binary classification experiment was The accuracy of the "opinion/non-opinion" binary classification experiment was 87.08% for FP_MinWiki and 74.72% for the Tohoku University model. These results indicate that even training data of about 720 sentences can be classified with more than 80% accuracy in binary classification, and that the characteristics of the data used for pre-training are reflected in the classification results.