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<p>Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despite extensive research, comprehensive surveys that critically synthesize the field from a unified perspective remain lacking. This survey makes several contributions beyond organizing existing work. We propose a unified taxonomy organized around four analytical perspectives that categorizes SRL research along model architectures, syntax feature modeling, application scenarios, and multimodal extensions. We provide a literature-based synthesis of when and why syntactic features help, identifying conditions under which syntax-aided approaches provide consistent gains over syntax-free counterparts. We offer a critical assessment of SRL in the era of large language models, examining the complementary roles of LLMs and specialized SRL systems and identifying directions for hybrid approaches. We extend the scope of SRL surveys to cover multimodal settings including visual, video, and speech modalities, and review structural differences in evaluation across these modalities. Methodologically, this work is a structured narrative survey rather than a formal systematic review: we describe explicit search procedures and inclusion criteria below, but do not follow PRISMA-style reporting protocols such as inter-reviewer screening or formal quality assessment of included studies. Literature was collected through structured searches of the ACL Anthology, IEEE Xplore, the ACM Digital Library, and Google Scholar, covering publications from 2000 to 2025, with the initial search completed in March 2025 and additional publications incorporated during the peer-review process through December 2025. Explicit inclusion and exclusion criteria were applied to yield approximately 200 primary references. SRL benchmarks, evaluation metrics, and paradigm modeling approaches are discussed alongside practical applications across domains. Future research directions are analyzed, addressing the evolving role of SRL with large language models and broader NLP impact.</p>

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A comprehensive survey of semantic role labeling in the era of pretrained language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月17日

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  • Huiyao Chen,
  • Meishan Zhang,
  • Jing Li,
  • Lilja Øvrelid,
  • Jan Hajič,
  • Hao Fei,
  • Min Zhang

摘要

Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despite extensive research, comprehensive surveys that critically synthesize the field from a unified perspective remain lacking. This survey makes several contributions beyond organizing existing work. We propose a unified taxonomy organized around four analytical perspectives that categorizes SRL research along model architectures, syntax feature modeling, application scenarios, and multimodal extensions. We provide a literature-based synthesis of when and why syntactic features help, identifying conditions under which syntax-aided approaches provide consistent gains over syntax-free counterparts. We offer a critical assessment of SRL in the era of large language models, examining the complementary roles of LLMs and specialized SRL systems and identifying directions for hybrid approaches. We extend the scope of SRL surveys to cover multimodal settings including visual, video, and speech modalities, and review structural differences in evaluation across these modalities. Methodologically, this work is a structured narrative survey rather than a formal systematic review: we describe explicit search procedures and inclusion criteria below, but do not follow PRISMA-style reporting protocols such as inter-reviewer screening or formal quality assessment of included studies. Literature was collected through structured searches of the ACL Anthology, IEEE Xplore, the ACM Digital Library, and Google Scholar, covering publications from 2000 to 2025, with the initial search completed in March 2025 and additional publications incorporated during the peer-review process through December 2025. Explicit inclusion and exclusion criteria were applied to yield approximately 200 primary references. SRL benchmarks, evaluation metrics, and paradigm modeling approaches are discussed alongside practical applications across domains. Future research directions are analyzed, addressing the evolving role of SRL with large language models and broader NLP impact.