论文标题
用于文档级事件参数提取的两潮AMR增强模型
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
论文作者
论文摘要
以前的大多数研究旨在从单个句子中提取事件，而文档级事件提取仍然不足。在本文中，我们专注于从整个文档中提取事件参数，该论点主要面临两个关键问题：a）触发器与句子的参数之间的长距离依赖性； b）对文档中事件的分心环境。为了解决这些问题，我们提出了一个两流的抽象含义表示增强的提取模型（TSAR）。沙皇从不同角度通过两条编码模块来编码文档，以利用本地和全局信息，并降低分心环境的影响。此外，TSAR还引入了AMR引导的相互作用模块，以捕获基于本地和全球构造的AMR语义图，捕获句内和句内特征。引入了辅助边界损失，以显式增强文本跨度的边界信息。广泛的实验表明，TSAR的表现可以超过先前的最新边距，而公共RAM和Wikivents数据集的2.54 F1和5.13 F1的性能提高，显示了跨阶段参数提取的优势。我们在https://github.com/ pkunlp-icler/tsar中发布代码。
Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/ PKUnlp-icler/TSAR.
