We present a novel document-level model for finding argument spans that fill
an event's roles, connecting related ideas in sentence-level semantic role
labeling and coreference resolution. Because existing datasets for
cross-sentence linking are small, development of our neural model is supported
through the creation of a new resource, Roles Across Multiple Sentences (RAMS),
which contains 9,124 annotated events across 139 types. We demonstrate strong
performance of our model on RAMS and other event-related datasets.

提出了一种新的文档级模型，用于找到填充事件角色的论点范围，在句子级语义角色标注和指代消解中连接相关思想。通过创建一个新的资源 Roles Across Multiple Sentences（RAMS），支持我们的神经模型的发展，该资源包含 139 种类型的 9,124 个注释事件。我们在 RAMS 和其他事件相关数据集上展示了我们的模型的强大性能。

多句话论点链接

Multi-Sentence Argument Linking

We present a simple and accurate span-based model for semantic role labeling
(SRL). Our model directly takes into account all possible argument spans and
scores them for each label. At decoding time, we greedily select higher scoring
labeled spans. One advantage of our model is to allow us to design and use
span-level features, that are difficult to use in token-based BIO tagging
approaches. Experimental results demonstrate that our ensemble model achieves
the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012
datasets, respectively.

本研究提出基于跨度的语义角色标注模型，能够直接考虑所有可能的论元跨度并为每个标签评分，模型具有允许设计和使用跨度级特征的优点。实验结果显示，本研究的集成模型在 CoNLL-2005 和 2012 数据集上实现了最先进结果，分别为 87.4 F1 和 87.0 F1。