Abstract
Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance.
Abstract (translated)
事件检测(ED)旨在识别无标签文本中的关键触发词，并预测相应的事件类型。传统的ED模型过于依赖数据，无法适应缺少标签数据的实际应用程序。此外，典型的ED模型正在面临由ED数据集引起的触发偏差引起的上下文绕过和 disabled 泛化问题。因此，我们关注真正的少数样本范式以满足资源有限的场景。特别是，我们提出了一种多步prompt learning模型( MsPrompt)，以消除少数样本事件检测中的偏见，该模型由以下三个组件组成：一个 under-sampling 模块旨在构建一个适应真正的少数样本设置的新的训练集，一个多步prompt模块配备了知识增强本体论，以利用PLM中的事件语义和潜在先验知识足够解决上下文绕过问题，一个典型模块用于弥补缺乏数据对分类事件的弱点并提高泛化性能。在ACE-2005和少数事件两个公共数据集上的实验表明， MsPrompt可以在与最佳性能基准相比加权F1得分下降11.43%的情况下表现更好，并实现出色的去偏见性能。
URL
https://arxiv.org/abs/2305.09335
