论文标题
广泛匹配几次学习事件检测
Extensively Matching for Few-shot Learning Event Detection
论文作者
论文摘要
超速学习设置下的当前事件检测模型无法转移到NewEvent类型。在事件检测中，很少有射击的学习尚未得到探索，即使它是一个模型，可以很好地在新事件类型上发挥高发电性。在这项工作中，我们将事件检测视为一些弹出的学习问题，以使事件检测到新事件类型。我们提出了两个Novelloss因素，这些因素与SUP港口中的示例相匹配，以向Themodel提供更多的培训信号。此外，这些培训信号可能会在许多基于公制的几杆学习模型中受到影响。我们在Theace-2005数据集（在几次学习的过程中）进行的广泛实验表明，所提出的方法可以激发少量学习的性能
Current event detection models under super-vised learning settings fail to transfer to newevent types. Few-shot learning has not beenexplored in event detection even though it al-lows a model to perform well with high gener-alization on new event types. In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel. Moreover, these training signals can beapplied in many metric-based few-shot learn-ing models. Our extensive experiments on theACE-2005 dataset (under a few-shot learningsetting) show that the proposed method can im-prove the performance of few-shot learning
