Semantic Role Labeling (SRL) aims at recognizing the predicate-argument
structure of a sentence and can be decomposed into two subtasks: predicate
disambiguation and argument labeling. Prior work deals with these two tasks
independently, which ignores the semantic connection between the two tasks. In
this paper, we propose to use the machine reading comprehension (MRC) framework
to bridge this gap. We formalize predicate disambiguation as multiple-choice
machine reading comprehension, where the descriptions of candidate senses of a
given predicate are used as options to select the correct sense. The chosen
predicate sense is then used to determine the semantic roles for that
predicate, and these semantic roles are used to construct the query for another
MRC model for argument labeling. In this way, we are able to leverage both the
predicate semantics and the semantic role semantics for argument labeling. We
also propose to select a subset of all the possible semantic roles for
computational efficiency. Experiments show that the proposed framework achieves
state-of-the-art or comparable results to previous work. Code is available at
https://github.com/ShannonAI/MRC-SRL.

本篇论文提出了一种利用机器阅读理解框架来解决语义角色标注中谓词消除和论元标注之间语义联系不足问题的方法，将谓词消歧形式化为多项选择机器阅读理解的形式，然后利用所选谓词的语义角色构造查询从而解决论元标注。实验证明该方法优于先前的工作。

语义角色标注的 MRC 框架

An MRC Framework for Semantic Role Labeling

Semantic role labeling (SRL) is to recognize the predicate-argument structure
of a sentence, including subtasks of predicate disambiguation and argument
labeling. Previous studies usually formulate the entire SRL problem into two or
more subtasks. For the first time, this paper introduces an end-to-end neural
model which unifiedly tackles the predicate disambiguation and the argument
labeling in one shot. Using a biaffine scorer, our model directly predicts all
semantic role labels for all given word pairs in the sentence without relying
on any syntactic parse information. Specifically, we augment the BiLSTM encoder
with a non-linear transformation to further distinguish the predicate and the
argument in a given sentence, and model the semantic role labeling process as a
word pair classification task by employing the biaffine attentional mechanism.
Though the proposed model is syntax-agnostic with local decoder, it outperforms
the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009
benchmarks for both English and Chinese. To our best knowledge, we report the
first syntax-agnostic SRL model that surpasses all known syntax-aware models.

本文介绍了一个端到端的神经模型，它统一处理谓语消歧和参数标记，并通过使用双仿射评分器直接预测句子中所有给定单词对的语义角色标签，达到了比现有的支持语法的 SRL 系统更好的性能。