2024/09/20 by Parisa Jamadi Khiabani, Khiabani, Parisa Jamadi, Arkaitz Zubiaga +1 · 2 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Infrared Target Detection Methodologies #Social and Information Networks (cs.SI) #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2409.13594
openalex publication_date 2024/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.