Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
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.
Code (1)
Tasks
Semantic Role LabelingSimilar Papers 제목 키워드 기반
Label Definitions Improve Semantic Role Labeling
Argument classification is at the core of Semantic Role Labeling. Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate. While semantic roles come with meaningful definit…
Semantic Role LabelingSentenceQuery Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identification and role classification. Such m…
ClassificationMulti-class ClassificationSemantic Role LabelingSemantic Role Labeling: A Systematical Survey
Semantic role labeling (SRL) is a central natural language processing (NLP) task aiming to understand the semantic roles within texts, facilitating a wide range of downstream applications. While SRL has garnered extensiv…
Semantic Role LabelingSurveyLearning Disentangled Representations in Natural Language Definitions with Semantic Role Labeling Supervision
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. However, most disentanglement methods …
DisentanglementSemantic Role LabelingSentenceAn Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling
Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty's feature-based view of proto-roles. This theory determines agent…
Semantic Role Labeling