Stance Reasoner：基于显式推理的社交媒体零样本立场检测
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
AI总结：
提出 Stance Reasoner 方法，利用预训练语言模型和思维链上下文学习进行显式推理，在零样本社交媒体立场检测任务中优于现有模型，实现更好的跨目标泛化并提供可解释的预测。
摘要：
社交媒体平台是富含观点内容的来源。立场检测能够从此类内容中自动提取用户对各种主题的意见。我们关注零样本立场检测，其模型的成功依赖于：(a) 具备关于目标主题的知识；以及 (b) 学习可用于新主题的通用推理策略。我们提出了 Stance Reasoner，这是一种针对社交媒体零样本立场检测的方法，利用对背景知识的显式推理来指导模型推断文档对特定目标的立场。具体而言，我们的方法使用预训练语言模型作为世界知识来源，并采用思维链上下文学习方法来生成中间推理步骤。Stance Reasoner 在 3 个 Twitter 数据集上优于当前最先进的模型，包括完全监督模型。它能更好地跨目标泛化，同时为其预测提供显式且可解释的解释。
英文摘要：
Social media platforms are rich sources of opinionated content. Stance detection allows the automatic extraction of users' opinions on various topics from such content. We focus on zero-shot stance detection, where the model's success relies on (a) having knowledge about the target topic; and (b) learning general reasoning strategies that can be employed for new topics. We present Stance Reasoner, an approach to zero-shot stance detection on social media that leverages explicit reasoning over background knowledge to guide the model's inference about the document's stance on a target. Specifically, our method uses a pre-trained language model as a source of world knowledge, with the chain-of-thought in-context learning approach to generate intermediate reasoning steps. Stance Reasoner outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. It can better generalize across targets, while at the same time providing explicit and interpretable explanations for its predictions.
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。
