论文标题
从Wikipedia中注入知识以增强立场检测
Infusing Knowledge from Wikipedia to Enhance Stance Detection
论文作者
论文摘要
立场检测侵入文本作者对目标的态度。当模型缺乏有关目标的背景知识时，这是具有挑战性的。在这里，我们展示了Wikipedia的背景知识如何帮助提高立场检测的表现。我们介绍了Wikipedia立场检测BERT（WS-BERT），将知识注入姿势编码。涵盖社交媒体讨论和在线辩论的三个基准数据集的广泛结果表明，我们的模型大大优于针对特定目标的立场检测，跨目标姿态检测以及零/几次射击立场检测的最新方法。
Stance detection infers a text author's attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.
