Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document. While recent work on document-level extraction has gone beyond single-sentence and increased the cross-sentence inference capability of end-to-end models, they are still restricted by certain input sequence length constraints and usually ignore the global context between events. To tackle this issue, we introduce a new global neural generation-based framework for document-level event argument extraction by constructing a document memory store to record the contextual event information and leveraging it to implicitly and explicitly help with decoding of arguments for later events. Empirical results show that our framework outperforms prior methods substantially and it is more robust to adversarially annotated examples with our constrained decoding design. (Our code and resources are available at https://github.com/xinyadu/memory_docie for research purpose.)
翻译:从新闻文章中提取事件的信息论据是信息提取方面一个具有挑战性的问题,需要从全球背景上理解每份文件。虽然最近关于文件提取的工作已经超出了单一判决的范围,提高了终端到终端模型的交叉判决推论能力,但是仍然受到某些输入序列长度限制的限制,通常忽视事件之间的全球背景。为了解决这一问题,我们为文件级活动提取争议引入一个新的全球神经新一代框架,方法是建立一个文件存储存储存储库,记录背景事件信息,并利用其为以后的事件以隐含和明确的方式帮助解码。 经验性结果显示,我们的框架大大超越了以往的方法,并且更有力地以对抗性的方式举例说明我们有限的解码设计。 (我们的代码和资源可以在https://github.com/xinyadu/memory_docie上查阅,用于研究目的。)