Stance detection is the view towards a specific target by a given context
(\textit{e.g.} tweets, commercial reviews). Target-related knowledge is often
needed to assist stance detection models in understanding the target well and
making detection correctly. However, prevailing works for knowledge-infused
stance detection predominantly incorporate target knowledge from a singular
source that lacks knowledge verification in limited domain knowledge. The
low-resource training data further increases the challenge for the data-driven
large models in this task. To address those challenges, we propose a
collaborative knowledge infusion approach for low-resource stance detection
tasks, employing a combination of aligned knowledge enhancement and efficient
parameter learning techniques. Specifically, our stance detection approach
leverages target background knowledge collaboratively from different knowledge
sources with the help of knowledge alignment. Additionally, we also introduce
the parameter-efficient collaborative adaptor with a staged optimization
algorithm, which collaboratively addresses the challenges associated with
low-resource stance detection tasks from both network structure and learning
perspectives. To assess the effectiveness of our method, we conduct extensive
experiments on three public stance detection datasets, including low-resource
and cross-target settings. The results demonstrate significant performance
improvements compared to the existing stance detection approaches.

我们提出了一种协作知识注入方法，用于低资源立场检测任务，通过知识对齐协作地利用来自不同知识源的目标背景知识，同时引入了参数高效的协作适配器和分阶段优化算法来解决低资源立场检测任务的挑战，实验结果表明相比现有的立场检测方法有显著的性能提升。