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

Hwiyeol Jo, Jaeseo Lim, Byoung-Tak Zhang


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.
Anthology ID:
2021.findings-emnlp.187
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2168–2174
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.187
DOI:
10.18653/v1/2021.findings-emnlp.187
Bibkey:
Cite (ACL):
Hwiyeol Jo, Jaeseo Lim, and Byoung-Tak Zhang. 2021. Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2168–2174, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification (Jo et al., Findings 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.187.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.187.mp4
Code
 hwiyeoljo/devilsadvocate
Data
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