@inproceedings{zheng-etal-2022-knowledge,
title = "Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection",
author = "Zheng, Kai and
Sun, Qingfeng and
Yang, Yaming and
Xu, Fei",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.83",
doi = "10.18653/v1/2022.findings-emnlp.83",
pages = "1168--1178",
abstract = "Stance Detection Task (SDT) aims at identifying the stance of the sentence towards a specific target and is usually modeled as a classification problem. Backgound knowledge is often necessary for stance detection with respect to a specific target, especially when there is no target explicitly mentioned in text. This paper focuses on the knowledge stimulation for low-resource stance detection tasks. We firstly explore to formalize stance detection as a prompt based contrastive learning task. At the same time, to make prompt learning suit to stance detection, we design a template mechanism to incorporate corresponding target into instance representation. Furthermore, we propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from the pre-trained model. The experimental results on three benchmarks show that knowledge stimulation is effective in stance detection accompanied with our proposed mechanism.",
}
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<abstract>Stance Detection Task (SDT) aims at identifying the stance of the sentence towards a specific target and is usually modeled as a classification problem. Backgound knowledge is often necessary for stance detection with respect to a specific target, especially when there is no target explicitly mentioned in text. This paper focuses on the knowledge stimulation for low-resource stance detection tasks. We firstly explore to formalize stance detection as a prompt based contrastive learning task. At the same time, to make prompt learning suit to stance detection, we design a template mechanism to incorporate corresponding target into instance representation. Furthermore, we propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from the pre-trained model. The experimental results on three benchmarks show that knowledge stimulation is effective in stance detection accompanied with our proposed mechanism.</abstract>
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%0 Conference Proceedings
%T Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection
%A Zheng, Kai
%A Sun, Qingfeng
%A Yang, Yaming
%A Xu, Fei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zheng-etal-2022-knowledge
%X Stance Detection Task (SDT) aims at identifying the stance of the sentence towards a specific target and is usually modeled as a classification problem. Backgound knowledge is often necessary for stance detection with respect to a specific target, especially when there is no target explicitly mentioned in text. This paper focuses on the knowledge stimulation for low-resource stance detection tasks. We firstly explore to formalize stance detection as a prompt based contrastive learning task. At the same time, to make prompt learning suit to stance detection, we design a template mechanism to incorporate corresponding target into instance representation. Furthermore, we propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from the pre-trained model. The experimental results on three benchmarks show that knowledge stimulation is effective in stance detection accompanied with our proposed mechanism.
%R 10.18653/v1/2022.findings-emnlp.83
%U https://aclanthology.org/2022.findings-emnlp.83
%U https://doi.org/10.18653/v1/2022.findings-emnlp.83
%P 1168-1178
Markdown (Informal)
[Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection](https://aclanthology.org/2022.findings-emnlp.83) (Zheng et al., Findings 2022)
ACL