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。

语义角色标注的跨度选择模型

A Span Selection Model for Semantic Role Labeling

Recent BIO-tagging-based neural semantic role labeling models are very high
performing, but assume gold predicates as part of the input and cannot
incorporate span-level features. We propose an end-to-end approach for jointly
predicting all predicates, arguments spans, and the relations between them. The
model makes independent decisions about what relationship, if any, holds
between every possible word-span pair, and learns contextualized span
representations that provide rich, shared input features for each decision.
Experiments demonstrate that this approach sets a new state of the art on
PropBank SRL without gold predicates.

本文提出一种全新的端到端方法，用于联合预测语义角色标签任务中的谓词、谓词指向的句子主语和宾语及它们之间的关系，并在 PropBank SRL 数据集上取得了最佳效果。