Current end-to-end semantic role labeling is mostly accomplished via
graph-based neural models. However, these all are first-order models, where
each decision for detecting any predicate-argument pair is made in isolation
with local features. In this paper, we present a high-order refining mechanism
to perform interaction between all predicate-argument pairs. Based on the
baseline graph model, our high-order refining module learns higher-order
features between all candidate pairs via attention calculation, which are later
used to update the original token representations. After several iterations of
refinement, the underlying token representations can be enriched with globally
interacted features. Our high-order model achieves state-of-the-art results on
Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile
relieving the long-range dependency issues.

本文呈献一种高级别的精细机制，通过注意力计算与所有谓词 - 论元对之间的交互来进行更新标记表示，以解决长程依赖问题，从而在中文 SRL 数据上实现了最先进的结果。

针对中文端到端语义角色标注的高阶细化

High-order Refining for End-to-end Chinese Semantic Role Labeling

Document-level relation extraction is a complex human process that requires
logical inference to extract relationships between named entities in text.
Existing approaches use graph-based neural models with words as nodes and edges
as relations between them, to encode relations across sentences. These models
are node-based, i.e., they form pair representations based solely on the two
target node representations. However, entity relations can be better expressed
through unique edge representations formed as paths between nodes. We thus
propose an edge-oriented graph neural model for document-level relation
extraction. The model utilises different types of nodes and edges to create a
document-level graph. An inference mechanism on the graph edges enables to
learn intra- and inter-sentence relations using multi-instance learning
internally. Experiments on two document-level biomedical datasets for
chemical-disease and gene-disease associations show the usefulness of the
proposed edge-oriented approach.

本研究采用面向边的图神经网络模型对文档级关系抽取进行研究，该模型利用不同类型的节点和边来创建文档级图，并通过图上边的推理机制进行联合及交叉句子的学习，实验结果表明该方法在化学疾病和基因疾病相关性数据集上表现出很好的性能。