EDDA：一种用于零样本立场检测的编码器-解码器数据增强框架
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
AI总结：
针对零样本立场检测中数据增强泛化不足的问题，提出编码器-解码器数据增强框架EDDA，利用大语言模型生成逻辑关联并增强句法多样性，显著提升了检测性能与可解释性。
摘要：
立场检测旨在确定文本中针对给定目标表达的态度。零样本立场检测（ZSSD）应运而生，用于在推理过程中对未见目标的立场进行分类。近期用于ZSSD的数据增强技术通过文本或目标增强来增加目标间的可迁移知识。然而，这些方法存在局限性。目标增强在生成的目标与源文本之间缺乏逻辑联系，而文本增强仅依赖训练数据，导致泛化能力不足。为解决这些问题，我们提出了一种编码器-解码器数据增强（EDDA）框架。编码器利用大语言模型和思维链提示，将文本总结为特定目标的if-then逻辑，建立逻辑关系。解码器使用语义关联词替换策略基于这些表达生成新样本，以增加句法多样性。我们还分析了生成的表达，开发了一个充分利用增强数据的逻辑增强网络。在基准数据集上的实验表明，我们的方法显著优于最先进的ZSSD技术。提出的EDDA框架提高了增强文本中的语义相关性和句法多样性，同时实现了可解释的基于逻辑的学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
