论文标题
语义角色标签符合定义建模：使用自然语言描述谓词题目结构
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
论文作者
论文摘要
过去和现在的语义角色标签方法的常见特征之一是，它们依赖于从预定义的语言库存中得出的离散标签来对谓词感官及其论点进行分类。但是，我们认为这不一定是这样。在本文中，我们提出了一种利用定义建模的方法，将SRL的广义公式作为使用自然语言定义而不是离散标签来描述谓词题目结构的任务。我们的新颖配方朝着赋予赋予可解释性和灵活性的第一步，但是我们对Propbank式和Framenet式的实验和分析，基于依赖关系和基于SPAN SRL的实验也表明，具有可解释输出的灵活模型并不一定以性能为代价。我们在https://github.com/sapienzanlp/dsrl上发布了用于研究目的的软件。
One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.
