跨目标立场检测十年进展综述，从统计方法到大模型的演进。
Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges
- 从传统统计方法进化到神经网络与大模型驱动的检测
- 引入话题分组注意力与对抗学习实现零样本检测
- 适合研究立场分析、跨领域情感挖掘的学者参考
立场检测旨在判断文本对特定目标所持观点。其中，跨目标立场检测关注模型在训练于某些目标后，能否有效应用于未见过的新目标。随着在线观点与意见分析需求激增，该任务近年来受到广泛关注。本文回顾过去十年跨目标立场检测的技术演进，涵盖从基础统计方法到现代神经网络与大模型的方法发展，显著提升了准确率与适应性。创新策略包括话题分组注意力与对抗学习用于零样本检测，以及微调、提示调优和外部知识融合等技术提升模型鲁棒性。同时系统梳理了常用评估数据集，揭示领域进展与现存挑战。最后指出未来研究方向与潜在突破路径。
原文摘要 · Abstract (English)
Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.
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