论文标题
文档级别的事件角色填充提取使用多粒性上下文化编码
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
论文作者
论文摘要
事件提取文献中很少有作品超越了单个句子来做出提取决策。当识别事件参数所需的信息分布在多个句子中时，这是有问题的。我们认为，文档级事件提取是一项艰巨的任务，因为它需要更大的上下文视图来确定哪些文本跨度与事件角色填充物相对应。我们首先研究端到端神经序列模型（具有预训练的语言模型表示）如何在文档级角色填充器提取中执行，以及捕获的上下文捕获的长度影响模型的性能。为了通过在不同级别的粒度（例如句子和段落级别）中学到的神经表示捕获的动态汇总信息，我们提出了一种新颖的多粒度读取器。我们在MUC-4事件提取数据集上评估了我们的模型，并表明我们的最佳系统的性能比以前的工作要好得多。我们还报告了关于任务上上下文长度与神经模型性能之间关系的发现。
Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models' performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.
