Stance detection aims to determine the attitude expressed in text towards a
given target. Zero-shot stance detection (ZSSD) has emerged to classify stances
towards unseen targets during inference. Recent data augmentation techniques
for ZSSD increase transferable knowledge between targets through text or target
augmentation. However, these methods exhibit limitations. Target augmentation
lacks logical connections between generated targets and source text, while text
augmentation relies solely on training data, resulting in insufficient
generalization. To address these issues, we propose an encoder-decoder data
augmentation (EDDA) framework. The encoder leverages large language models and
chain-of-thought prompting to summarize texts into target-specific if-then
rationales, establishing logical relationships. The decoder generates new
samples based on these expressions using a semantic correlation word
replacement strategy to increase syntactic diversity. We also analyze the
generated expressions to develop a rationale-enhanced network that fully
utilizes the augmented data. Experiments on benchmark datasets demonstrate our
approach substantially improves over state-of-the-art ZSSD techniques. The
proposed EDDA framework increases semantic relevance and syntactic variety in
augmented texts while enabling interpretable rationale-based learning.

我们提出了一种基于编码器 - 解码器的数据增强框架，通过逻辑关系以及语义相关性的词替换策略，增加了语义相关性和句法多样性，并使得数据在进行神经网络训练时变得可解释。在基准数据集上的实验表明，我们的方法在零样本情况下的立场检测明显优于现有技术。