Span-based Semantic Role Labeling as Lexicalized Constituency Tree Parsing

ACL ARR 2025 February Submission4237 Authors

Abstract: Semantic Role Labeling (SRL) is a critical task that focuses on identifying predicate-argument structures in sentences. Span-based SRL, a prominent paradigm, is often tackled using BIO-based or graph-based methods. However, these approaches often fail to capture the inherent relationship between syntax and semantics. While syntax-aware models have been proposed to address this limitation, they heavily rely on pre-existing syntactic resources, limiting their general applicability. In this work, we propose a lexicalized tree representation for span-based SRL, which integrates constituency and dependency parsing to explicitly model predicate-argument structures. By structurally representing predicates as roots and arguments as subtrees directly linked to the predicate, our approach bridges the gap between syntactic and semantic representations. Experiments on standard benchmarks (CoNLL05 and CoNLL12) demonstrate that our model achieves competitive performance, with particular improvement in predicate-given settings.

Paper Type: Long

Research Area: Syntax: Tagging, Chunking and Parsing

Research Area Keywords: semantic role labeling, semantic parsing

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 4237