论文标题
零射击立场检测：使用通用主题表示形式的数据集和模型
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
论文作者
论文摘要
立场检测是理解日常生活中隐藏影响的重要组成部分。由于有成千上万的潜在主题需要采取立场，大多数人几乎没有培训数据，因此我们专注于零射击姿势检测：从没有培训示例中对立场进行分类。在本文中，我们提出了一个用于零射击姿势检测的新数据集，该数据集比以前的数据集中捕获更广泛的主题和词汇变化。此外，我们提出了一个新的模型来进行立场检测，该模型隐含使用广义主题表示之间捕获主题之间的关系，并表明该模型改善了许多具有挑战性的语言现象的性能。
Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.
