论文标题
很少示出文档级事件参数提取
Few-Shot Document-Level Event Argument Extraction
论文作者
论文摘要
事件参数提取（EAE）在句子级别进行了很好的研究，但在文档级别进行了探讨。在本文中，我们研究以捕获实际上分布在文档中的句子中的事件论点。先前的工作通常会完全访问丰富的文档监督，而忽略了通常的参数注释通常受到限制的事实。为了填补这一空白，我们基于现有的文档级事件提取数据集，介绍了几个Docae，几个弹药级的事件参数提取基准。我们首先定义了新问题，并通过新颖的N-Way-D-Doc采样而不是传统的N-Way-K-shot策略来重建语料库。然后，我们将当前的文档级神经模型调整为几个射击设置，以在内部和跨域设置下提供基线结果。由于论点提取取决于多个句子的上下文，并且学习过程仅限于很少的示例，因此我们发现这项新颖的任务非常具有挑战性。考虑到很少有Docae与低资源制度下的实际使用密切相关，我们希望这种基准能够朝着这一方向进行更多的研究。我们的数据和代码将在线提供。
Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. In this paper, we study to capture event arguments that actually spread across sentences in documents. Prior works usually assume full access to rich document supervision, ignoring the fact that the available argument annotation is usually limited. To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset. We first define the new problem and reconstruct the corpus by a novel N -Way-D-Doc sampling instead of the traditional N -Way-K-Shot strategy. Then we adjust the current document-level neural models into the few-shot setting to provide baseline results under in- and cross-domain settings. Since the argument extraction depends on the context from multiple sentences and the learning process is limited to very few examples, we find this novel task to be very challenging with substantively low performance. Considering FewDocAE is closely related to practical use under low-resource regimes, we hope this benchmark encourages more research in this direction. Our data and codes will be available online.
