论文标题
语言独立立场检测：基于社会互动的嵌入和大语言模型
Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models
论文作者
论文摘要
即使许多基准基于Twitter等社交网络数据，大多数在立场检测上进行的研究都集中在开发或多或少复杂的文本分类系统上。本文旨在通过将重点放在文本本身，而是社交网络上可用的交互数据上来承担立场检测任务。更具体地说，我们提出了一种新方法，通过生成关系嵌入，即互动对的密集矢量表示，以利用社会信息，例如朋友和转发。我们对七个公开可用的数据集和四种不同语言（巴斯克，加泰罗尼亚，意大利语和西班牙语）进行的实验表明，将我们的关系嵌入与判别性文本方法相结合有助于实质上提高性能，从而获得六个评估中的六个评估设置中的六个最先进的结果，从而超过了基于大型语言模型的强大模型，例如基于大型语言模型或其他流行的InterActions或其他流行的方法，例如或其他流行的方法。
The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on the text itself but on the interaction data available on social networks. More specifically, we propose a new method to leverage social information such as friends and retweets by generating Relational Embeddings, namely, dense vector representations of interaction pairs. Our experiments on seven publicly available datasets and four different languages (Basque, Catalan, Italian, and Spanish) show that combining our relational embeddings with discriminative textual methods helps to substantially improve performance, obtaining state-of-the-art results for six out of seven evaluation settings, outperforming strong baselines based on Large Language Models, or other popular interaction-based approaches such as DeepWalk or node2vec.
