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        <title>Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection</title>
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    <abstract>Stance detection aims to identify whether the author of an opinionated text is in favor of, against, or neutral towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, it is labor-intensive to annotate sufficient data and train the model for every new target.Therefore, zero-shot stance detection, aiming at identifying stances of unseen targets with seen targets, has gradually attracted attention. Among them, one of the important challenges is to reduce the domain transfer between seen and unseen targets. To tackle this problem, we propose a generative data augmentation approach to generate training samples containing targets and stances for testing data, and map the real samples and generated synthetic samples into the same embedding space with contrastive learning, then perform the final classification based on the augmented data. We evaluate our proposed model on two benchmark datasets. Experimental results show that our approach achieves state-of-the-art performance on most topics in the task of zero-shot stance detection.</abstract>
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%0 Conference Proceedings
%T Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection
%A Li, Yang
%A Yuan, Jiawei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-yuan-2022-generative
%X Stance detection aims to identify whether the author of an opinionated text is in favor of, against, or neutral towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, it is labor-intensive to annotate sufficient data and train the model for every new target.Therefore, zero-shot stance detection, aiming at identifying stances of unseen targets with seen targets, has gradually attracted attention. Among them, one of the important challenges is to reduce the domain transfer between seen and unseen targets. To tackle this problem, we propose a generative data augmentation approach to generate training samples containing targets and stances for testing data, and map the real samples and generated synthetic samples into the same embedding space with contrastive learning, then perform the final classification based on the augmented data. We evaluate our proposed model on two benchmark datasets. Experimental results show that our approach achieves state-of-the-art performance on most topics in the task of zero-shot stance detection.
%R 10.18653/v1/2022.emnlp-main.470
%U https://aclanthology.org/2022.emnlp-main.470
%U https://doi.org/10.18653/v1/2022.emnlp-main.470
%P 6985-6995

Markdown (Informal)

[Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection](https://aclanthology.org/2022.emnlp-main.470) (Li & Yuan, EMNLP 2022)

• Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (Li & Yuan, EMNLP 2022)

ACL

• Yang Li and Jiawei Yuan. 2022. Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6985–6995, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.