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A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models

Paper · 2025 · Huiyao Chen, Meishan Zhang, Jing Li et al.

Summary

This survey organizes about 25 years of semantic role labeling (SRL) research, from Gildea and Jurafsky (2000) to work in the LLM era. SRL is the task of identifying, for a given predicate, the participants involved (the actor and the entity acted upon) along with the time, location and manner of the event. The authors searched four databases (ACL Anthology, IEEE Xplore, ACM Digital Library, Google Scholar) and applied explicit inclusion and exclusion rules, ending with roughly 200 core papers. They sort the field along four axes: model architecture, use of syntactic features, application setting, and multimodal extensions. This framing lets FrameNet-style and PropBank-style work be compared side by side.

Beyond the taxonomy, the survey:

  • examines when explicit syntax actually helps compared with syntax-free models,
  • describes how LLMs and dedicated SRL systems complement each other,
  • covers SRL for images, video and speech,
  • adds an error analysis and a guide for choosing a method.

The authors conclude that SRL remains valuable even next to LLMs. It is a checkable structured semantic layer and a probe of what foundation models really understand. The most promising path is hybrid: LLM representations combined with the precision of specialized systems.

Key points

  • The corpus covers publications from 2000 to 2025, about 200 primary references, and spans textual, visual, video and speech SRL plus related tasks (AMR, event extraction).
  • Three annotation paradigms are described:
    • FrameNet: frames and frame elements, with possibly discontinuous spans.
    • PropBank: per-predicate rolesets with ARG0, ARG1 and so on, rooted in Levin-style verb classes and extended cross-lingually by Universal PropBank.
    • NomBank: nominal predicates mapped to their verbs through NOMLEX.
  • The milestones run from early syntax-feature systems, to Collobert et al. (2011) as the first end-to-end neural SRL, to seq2seq and generative models, and finally to LLMs.
  • The listed future directions include:
    • cross-modal alignment objectives and a unified multimodal evaluation protocol,
    • efficient, deployable SRL (distillation, quantization, incremental inference),
    • discourse-level SRL for implicit arguments.
  • Resources are collected in a maintained repository: github.com/DreamH1gh/Awesome-SRL.

Interesting ideas

  • I1. Prompted LLMs give outputs that vary from run to run, while dedicated architectures with deterministic decoding give stable structures that downstream pipelines can rely on; the survey therefore prefers hybrids over plain prompting.
  • I2. NomBank treats nominalizations as keeping the argument structure of their base verbs, using a lexicon that links each noun to its verb.
  • I3. The authors view SRL as a diagnostic layer for testing whether a large model really grasps predicate-argument meaning, not only as a component of a pipeline.
  • I4. Discourse SRL aims to fill implicit arguments and connect roles across sentence boundaries, not just within one clause.
  • I5. Question-answer driven crowdsourcing and pre-annotation tools are suggested as cheaper ways to obtain role annotations.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Keep LLMs in the teacher tier for producing or adjudicating silver role labels, and ship only the deterministic frame builder, so every answer can be replayed and explained.
  • I2 (frames): Map nouns like "destruction" or "purchase" to their VerbNet verb classes so that a phrase like "the city's destruction by the army" yields the same agent and patient frame as the verbal clause.
  • I3 (learn): Use role-labeled probe sentences, including alternation pairs where the roles stay fixed but the syntax changes, as negative-control tests for both the teacher LLMs and Mova's own frame output.
  • I4 (world): When a frame leaves a core role empty, look in the world of the text for a recent entity that fits the role's constraints, and mark the filled slot as inferred so the explanation stays honest.
  • I5 (learn): Ask the LLM teacher simple wh-questions about a sentence (who bought? what was bought?) and align its short answers with tree spans, producing silver role data that is easier to cross-check than raw labels.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore180 works · 34,304 citations🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations🎓Charles UniversityPrague · Czechia133 works · 20,686 citations🎓University of OsloOslo · Norway51 works · 4,155 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSemantic role labeling · 145 works

Cite

Cite as Huiyao Chen, Meishan Zhang, Jing Li, Lilja \Ovrelid, Jan Hajič, Hao Fei and Min Zhang. 2025. A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models. arXiv:2502.08660.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2502.08660 · arXiv 2502.08660

@misc{chen2025systematic,
  title = {{A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models}},
  author = {Huiyao Chen, Meishan Zhang, Jing Li, Lilja Ovrelid, Jan Hajivc, Hao Fei and Min Zhang},
  year = {2025},
  doi = {10.48550/arxiv.2502.08660},
  eprint = {2502.08660},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2502.08660}
}

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