Devil's Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification

0Citations
Citations of this article
40Readers
Mendeley users who have this article in their library.

Abstract

We present a new form of ensemble method- Devil's Advocate, which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate. Our method consists of two different training settings: one follows the conventional training process (Norm), and the other is trained by artificially generated labels (DevAdv). After training the models, Norm models are fine-tuned through an additional loss function, which uses the DevAdv model as a constraint. In making a final decision, the proposed ensemble model sums the scores of Norm models and then subtracts the score of the DevAdv model. The DevAdv model improves the overall performance of the other models within the ensemble. In addition to our ensemble framework being based on psychological background, it also shows comparable or improved performance on 5 text classification tasks when compared to conventional ensemble methods.

Cite

CITATION STYLE

APA

Jo, H., Lim, J., & Zhang, B. T. (2021). Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification. In Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 (pp. 2168–2174). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.187

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free