Unsupervised Ensemble of Ranking Models for News Comments Using Pseudo Answers

Ranking comments on an online news service is a practically important task, and thus there have been many studies on this task. Although ensemble techniques are widely known to improve the performance of models, there is little types of research on ensemble neural-ranking models. In this paper, we investigate how to improve the performance on the comment-ranking task by using unsupervised ensemble methods. We propose a new hybrid method composed of an output selection method and a typical averaging method. Our method uses a pseudo answer represented by the average of multiple model outputs. The pseudo answer is used to evaluate multiple model outputs via ranking evaluation metrics, and the results are used to select and weight the models. Experimental results on the comment-ranking task show that our proposed method outperforms several ensemble baselines, including supervised one.