Zero-Shot Stance Detection (ZSSD) identifies the attitude of the post toward unseen targets. Existing research using contrastive, meta-learning, or data augmentation suffers from generalizability issues or lack of coherence between text and target. Recent works leveraging large language models (LLMs) for ZSSD focus either on improving unseen target-specific knowledge or generating explanations for stance analysis. However, most of these works are limited by their over-reliance on explicit reasoning, provide coarse explanations that lack nuance, and do not explicitly model the reasoning process, making it difficult to interpret the model's predictions. To address these issues, in our study, we develop a novel interpretable ZSSD framework, IRIS. We provide an interpretable understanding of the attitude of the input towards the target implicitly based on sequences within the text (implicit rationales) and explicitly based on linguistic measures (explicit rationales). IRIS considers stance detection as an information retrieval ranking task, understanding the relevance of implicit rationales for different stances to guide the model towards correct predictions without requiring the ground-truth of rationales, thus providing inherent interpretability. In addition, explicit rationales based on communicative features help decode the emotional and cognitive dimensions of stance, offering an interpretable understanding of the author's attitude towards the given target. Extensive experiments on the benchmark datasets of VAST, EZ-STANCE, P-Stance, and RFD using 50%, 30%, and even 10% training data prove the generalizability of our model, benefiting from the proposed architecture and interpretable design.

翻译：零样本立场检测旨在识别文本对未见目标的立场态度。现有研究采用对比学习、元学习或数据增强等方法，存在泛化能力不足或文本与目标间连贯性缺失的问题。近期利用大语言模型进行零样本立场检测的研究，主要集中于提升对未见目标的特定知识理解或生成立场分析的解释。然而，这些工作大多过度依赖显式推理，提供的解释较为粗略、缺乏细微差别，且未显式建模推理过程，导致模型预测难以解释。为解决这些问题，本研究提出了一种新颖的可解释零样本立场检测框架IRIS。该框架基于文本内部序列（隐式依据）和语言学度量（显式依据），分别从隐式和显式两个维度提供对输入文本针对目标态度的可解释性理解。IRIS将立场检测视为信息检索排序任务，通过理解不同立场对应的隐式依据相关性来引导模型做出正确预测，且无需真实依据标注，从而提供内在可解释性。此外，基于交际特征的显式依据有助于解码立场的情感与认知维度，为作者对给定目标的态度提供可解释性理解。在VAST、EZ-STANCE、P-Stance和RFD基准数据集上，使用50%、30%甚至10%训练数据进行的广泛实验证明了本模型的泛化能力，这得益于所提出的架构与可解释性设计。