@inproceedings{li-caragea-2023-distilling,
title = "Distilling Calibrated Knowledge for Stance Detection",
author = "Li, Yingjie and
Caragea, Cornelia",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.393",
doi = "10.18653/v1/2023.findings-acl.393",
pages = "6316--6329",
abstract = "Stance detection aims to determine the position of an author toward a target and provides insights into people{'}s views on controversial topics such as marijuana legalization. Despite recent progress in this task, most existing approaches use hard labels (one-hot vectors) during training, which ignores meaningful signals among categories offered by soft labels. In this work, we explore knowledge distillation for stance detection and present a comprehensive analysis. Our contributions are: 1) we propose to use knowledge distillation over multiple generations in which a student is taken as a new teacher to transfer knowledge to a new fresh student; 2) we propose a novel dynamic temperature scaling for knowledge distillation to calibrate teacher predictions in each generation step. Extensive results on three stance detection datasets show that knowledge distillation benefits stance detection and a teacher is able to transfer knowledge to a student more smoothly via calibrated guiding signals. We publicly release our code to facilitate future research.",
}
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%0 Conference Proceedings
%T Distilling Calibrated Knowledge for Stance Detection
%A Li, Yingjie
%A Caragea, Cornelia
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-caragea-2023-distilling
%X Stance detection aims to determine the position of an author toward a target and provides insights into people’s views on controversial topics such as marijuana legalization. Despite recent progress in this task, most existing approaches use hard labels (one-hot vectors) during training, which ignores meaningful signals among categories offered by soft labels. In this work, we explore knowledge distillation for stance detection and present a comprehensive analysis. Our contributions are: 1) we propose to use knowledge distillation over multiple generations in which a student is taken as a new teacher to transfer knowledge to a new fresh student; 2) we propose a novel dynamic temperature scaling for knowledge distillation to calibrate teacher predictions in each generation step. Extensive results on three stance detection datasets show that knowledge distillation benefits stance detection and a teacher is able to transfer knowledge to a student more smoothly via calibrated guiding signals. We publicly release our code to facilitate future research.
%R 10.18653/v1/2023.findings-acl.393
%U https://aclanthology.org/2023.findings-acl.393
%U https://doi.org/10.18653/v1/2023.findings-acl.393
%P 6316-6329
Markdown (Informal)
[Distilling Calibrated Knowledge for Stance Detection](https://aclanthology.org/2023.findings-acl.393) (Li & Caragea, Findings 2023)
ACL