Semantic role labeling (SRL) is a task to recognize all the
predicate-argument pairs of a sentence, which has been in a performance
improvement bottleneck after a series of latest works were presented. This
paper proposes a novel syntax-agnostic SRL model enhanced by the proposed
associated memory network (AMN), which makes use of inter-sentence attention of
label-known associated sentences as a kind of memory to further enhance
dependency-based SRL. In detail, we use sentences and their labels from train
dataset as an associated memory cue to help label the target sentence.
Furthermore, we compare several associated sentences selecting strategies and
label merging methods in AMN to find and utilize the label of associated
sentences while attending them. By leveraging the attentive memory from known
training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark
datasets for syntax-agnostic setting, showing a new effective research line of
SRL enhancement other than exploiting external resources such as well
pre-trained language models.

本文提出了一种基于所提出的关联记忆网络的新颖的语法无关的语义角色标注模型，利用标签已知的相关句子的跨句子注意力作为一种记忆进一步增强基于依存的语义角色标注，并且通过利用已知训练数据的注意力记忆，我们的模型取得了 CoNLL-2009 基准数据集上的最新成果，在增强 SR L 方面展示了一个新的有效研究方向，而不是利用外部资源，如预先训练的语言模型。

关联记忆网络的语义角色标注

Semantic Role Labeling with Associated Memory Network

We introduce a simple and accurate neural model for dependency-based semantic
role labeling. Our model predicts predicate-argument dependencies relying on
states of a bidirectional LSTM encoder. The semantic role labeler achieves
competitive performance on English, even without any kind of syntactic
information and only using local inference. However, when automatically
predicted part-of-speech tags are provided as input, it substantially
outperforms all previous local models and approaches the best reported results
on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish
where our approach also achieves competitive results. Syntactic parsers are
unreliable on out-of-domain data, so standard (i.e., syntactically-informed)
SRL models are hindered when tested in this setting. Our syntax-agnostic model
appears more robust, resulting in the best reported results on standard
out-of-domain test sets.

本篇论文中介绍了一种用于词语语义角色标注的简单而准确的神经模型，该模型通过双向 LSTM 编码器的状态预测谓词论元依赖关系，即使没有任何语法信息，仅使用本地推理即可在英语上取得有竞争力的性能，然而，当自动生成词性标注作为输入时，它的性能明显优于所有先前的本地模型，并接近报告的英语 CoNLL-2009 数据集的最佳结果。我们还考虑了中文、捷克语和西班牙语，其中我们的方法也取得了有竞争力的结果。基于语法的解析器在领域外数据上不可靠，因此当在该设置中进行测试时，标准（即语法相关）的 SRL 模型受到了阻碍。我们的基于语法不可知的模型表现更健壮，这在标准的领域外测试集上得到了最佳的报告结果。