论文标题
反应性监督：一种收集讽刺数据的新方法
Reactive Supervision: A New Method for Collecting Sarcasm Data
论文作者
论文摘要
讽刺检测是情感计算的重要任务，需要大量标记的数据。我们介绍了反应性监督，这是一种新颖的数据收集方法，它利用在线对话的动态来克服现有数据收集技术的局限性。我们使用新方法来创建和发布具有讽刺透视标签和新的上下文功能的推文的首个大型数据集。该数据集有望推进讽刺检测研究。我们的方法可以适应其他情感计算领域，从而为新的研究机会开放。
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.
