Basic Ensemble Learning of Encoder Representations from Transformer for Disaster-mentioning Tweets Classification

Abstract

Ensemble learning is a system which used to train multiple learning models and combine their results, treating them as a “committee” of decision makers. To explore effect of ensemble learning, this paper applied two basic ensemble systems of encoder to natural language processing. To compare the individual models and ensemble systems, this paper varied the number models which used to calculate ensemble accuracies. The result is that the decision of the model, with all models combined, usually have better overall accuracy, on average, than any single model. It shown that ensemble system used all models usually have better performance. This paper given explanation in the conclusion section of this result.