Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-trivial due to the graph structure of standard knowledge bases. To overcome this, we explore an approach to integrate contextual information as text which allows for integrating contextual information from heterogeneous sources, such as structured knowledge sources and by prompting large language models. Our approach can outperform competitive baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training. We demonstrate that it is more robust to noisy context and can regularize for unwanted correlations between labels and target-specific vocabulary. Finally, it is independent of the pretrained language model in use.

翻译：立场检测旨在识别作者针对特定目标的立场。现有大多数立场检测模型存在局限性，因为它们未考虑有助于正确推断立场的相关上下文信息。互补性上下文可从知识库中获取，但由于标准知识库的图结构特性，将上下文集成到预训练语言模型中并非易事。为解决这一问题，我们探索了一种以文本形式集成上下文信息的方法，从而能够融合来自异构源（如结构化知识源及通过提示大型语言模型生成的信息）的上下文信息。在跨目标设置（即针对训练中未见过的目标）下，本方法在大型且多样化的立场检测基准测试中可超越具有竞争力的基线模型。我们证明该方法对噪声上下文具有更强的鲁棒性，并能正则化标签与目标特定词汇之间的非期望关联。最后，该方法不依赖于所使用的特定预训练语言模型。