论文标题
利用支持集中的匹配信息进行几个射击事件分类
Exploiting the Matching Information in the Support Set for Few Shot Event Classification
论文作者
论文摘要
现有的事件分类（EC）工作主要集中在传统的监督学习环境中，其中模型是Unableto提取事件事件提及了新的/看不见的事件类型。尽管它使EC模型可以将其操作扩展到未观察到的事件类型，但在该领域没有研究。为了填补这一差距，在这项工作中，我们调查了几次学习集的事件分类。我们为此问题提出了一种新颖的培训方法，该方法在几个射击模型的训练过程中大量利用了支持集。特别是，除了将查询考试与培训支持设置的查询检查匹配外，我们还试图进一步匹配支持设置的支持示例。该方法为模型提供了更多的培训信号，可以应用于每种基于指标的几次学习方法。我们广泛的实验Ontwo基准EC数据集表明，该建议的方法可以改善最佳报告的少量学习模型，最多可用于事件分类的精确度高达10％
The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types. Few-shot learninghas not been investigated in this area although it enables EC models toextend their operation to unobserved event types. To fill in this gap, inthis work, we investigate event classification under the few-shot learningsetting. We propose a novel training method for this problem that exten-sively exploit the support set during the training process of a few-shotlearning model. In particular, in addition to matching the query exam-ple with those in the support set for training, we seek to further matchthe examples within the support set themselves. This method providesmore training signals for the models and can be applied to every metric-learning-based few-shot learning methods. Our extensive experiments ontwo benchmark EC datasets show that the proposed method can improvethe best reported few-shot learning models by up to 10% on accuracyfor event classification
