Recently, semantic role labeling (SRL) has earned a series of success with
even higher performance improvements, which can be mainly attributed to
syntactic integration and enhanced word representation. However, most of these
efforts focus on English, while SRL on multiple languages more than English has
received relatively little attention so that is kept underdevelopment. Thus
this paper intends to fill the gap on multilingual SRL with special focus on
the impact of syntax and contextualized word representation. Unlike existing
work, we propose a novel method guided by syntactic rule to prune arguments,
which enables us to integrate syntax into multilingual SRL model simply and
effectively. We present a unified SRL model designed for multiple languages
together with the proposed uniform syntax enhancement. Our model achieves new
state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages.
Besides, we pose a discussion on the syntactic role among different languages
and verify the effectiveness of deep enhanced representation for multilingual
SRL.

本研究关注于多语言语义角色标注（SRL）模型的开发，并围绕句法规则、上下文化词表示等因素展开了研究，提出了一种基于句法规则的剪枝方法，并在 CoNLL-2009 的七种语言基准测试中实现了状态 - of-the-art 的效果，并验证了深度增强表示对多语言 SRL 的有效性。

面向语法的多语言语义角色标注

Syntax-aware Multilingual Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying the
predicate-argument structure of a sentence. It is typically regarded as an
important step in the standard NLP pipeline. As the semantic representations
are closely related to syntactic ones, we exploit syntactic information in our
model. We propose a version of graph convolutional networks (GCNs), a recent
class of neural networks operating on graphs, suited to model syntactic
dependency graphs. GCNs over syntactic dependency trees are used as sentence
encoders, producing latent feature representations of words in a sentence. We
observe that GCN layers are complementary to LSTM ones: when we stack both GCN
and LSTM layers, we obtain a substantial improvement over an already
state-of-the-art LSTM SRL model, resulting in the best reported scores on the
standard benchmark (CoNLL-2009) both for Chinese and English.

本文采用了图卷积神经网络的方法来处理句子的语法依存树，相比仅使用 LSTM 的方法，该方法表现出显著提升，并在中英文的 CoNLL-2009 标准测试中取得了最佳成绩。