Span-based Semantic Role Labeling as Lexicalized Constituency Tree Parsing (2025)

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 English benchmarks (CoNLL05 and CoNLL12) demonstrate that our model achieves competitive performance, with particular improvement in predicate-given settings.

## introduction semantic role labeling (srl) is a fundamental task in natural language processing, aiming to identify the arguments of predicates in a sentence and assign them appropriate semantic roles. there are two dominant paradigms in srl: word-based (dependency-based) srl, which assigns roles to individual words, and span-based srl, which identifies multi-word argument spans. this paper focuses on span-based srl. span-based srl is typically approached through two primary methods: bio-based tagging and (span-based) graph parsing. the bio-based approach formulates srl as a sequence labeling task @xcite , where each word in the sentence is assigned one * corresponding author of three labels {b, i, o}, denoting the beginning, inside, or outside of an argument span. to ensure the coherence of argument spans, structural constraints, such as linear-chain conditional random field (crf), are applied. on the other hand, graph parsing directly identifies predicate-argument pairs by linking argument spans to predicates @xcite . while this method excels at capturing the full scope of predicate-argument structures, it faces significant challenges, including large search spaces (o(n 3 )) and potential conflicts between argument spans due to the absence of structural constraints. a long-standing hypothesis in srl research is that syntactic information plays a critical role in accurate predicate-argument identification and classification @xcite . syntactic structures provide key insights into predicateargument structures, as evidenced in propbankstyle srl @xcite , where arguments are often syntactic constituents. additionally, the process of establishing semantic relationships between predicates and arguments mirrors the construction of dependency relationships between words. these observations have led to the development of syntax-aware srl models that leverage syntactic information, such as dependency and constituency trees, to improve srl performance. syntax-aware models typically involve using syntactic trees to guide argument pruning @xcite , integrating syntactic features derived from these trees @xcite , or employing multitask learning frameworks to jointly learn syntax and semantics @xcite . these approaches consistently demonstrate that incorporating syntactic knowledge leads to improved srl performance. however, a significant limitation of most existing syntax-aware srl models is their reliance on pre-existing syntactic resources, hindering their applicability to low-resource domains and languages. recent advances in structured representations for span-based srl have sought to address this limitation by directly modeling argument structures with tree-based representations. @xcite introduced a framework where argument structures are represented as constituent trees, with each argument span corresponding to a tree node. similarly, @xcite explored the internal structures of argument spans, treating them as latent subtrees in a dependency representation. these approaches highlight the potential of structured representations to effectively capture the hierarchical and relational nature of predicate-argument structures. in this work, we introduce a novel lexicalized tree representation for span-based srl, seamlessly integrating the strengths of constituency and dependency parsing. a lexicalized tree captures the local domain of a headword-in this case, the predicate along with all its semantic arguments. this allows predicate-argument structures to be naturally modeled as (flat) lexicalized trees, where the predicate serves as the root, arguments are organized as constituent subtrees, and dependencies explicitly link arguments to the predicate. our treebased representation preserves the full complexity of predicate-argument structures, akin to graphbased models, while maintaining the coherence of argument spans, as seen in bio-based models. our main contributions are as follows: ## proposed method this section presents our proposed method for spanbased srl using a lexicalized tree representation. we begin by introducing the foundational concepts of lexicalized trees, followed by detailing the construction of lexicalized trees from span-based srl and their conversion into predicate-argument structures. ## model in this section, we describe the detailed implementation of our lexicalized parsing model. our approach consists of four key components: (1) scoring mechanism, (2) model architecture, (3) training, and (4) inference. ## conclusion in this work, we introduce a novel lexicalized tree representation for span-based srl, which integrates constituency and dependency parsing to model predicate-argument structures effectively. by structurally representing predicates as roots and arguments as subtrees explicitly linked to the predi-cate, our approach bridges the gap between syntactic and semantic representations. experiments on standard benchmarks (conll05 and conll12) demonstrate that our model achieves competitive performance, particularly excelling in predicategiven settings. the ablation studies and analysis of predicate-argument structures further validate the effectiveness of our approach in capturing complex semantic relationships.