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A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research

Paper · 2026 · Divyesh Pratap Singh, Dakshesh Gusain, Federica Bulgarelli et al.

Summary

Developmental studies suggest that the balance between manner verbs (how an action is done, e.g., rub) and result verbs (what outcome occurs, e.g., fill) may distinguish late talkers from typically developing children. Researchers cannot yet study this contrast in large corpora because no sizable annotated resource exists. The authors fill this gap in two steps. First, they use large language models as annotators, guided by prompts built on established linguistic diagnostics. These diagnostics include object omission, the causative/inchoative alternation, telicity, and scalar versus non-scalar change. The LLMs labeled sentences from MASC and InterCorp. Second, the authors fine-tuned a RoBERTa classifier on these labels. They then tested it on three held-out gold-standard sets, one of which is newly annotated by an expert. The classifier reaches an average accuracy of up to 89.6%. The analysis also indicates that the semantics of the verb root matter more than syntactic cues for this task. The authors present the system as a scalable research instrument for corpus studies of verb meaning, such as caregiver and child speech, and not as a clinical tool.

Key points

  • LLM-generated annotations extend manner/result coverage from 151 previously labeled VerbNet classes to 436 classes.
  • Training data come from MASC and the InterCorp parallel corpus. The model is a fine-tuned RoBERTa classifier.
  • Evaluation uses three held-out gold-standard datasets and reports up to 89.6% average accuracy.
  • The study has several limitations:
    • Polysemous verbs and verbs with mixed manner/result meaning (e.g., drown, braise) were not handled.
    • The data are mainly standard English.
    • The expert gold set was built by essentially one annotator, so inter-rater reliability is unavailable.
    • Expert spot checks found the LLM labels to be imperfect.
  • The authors plan to release the code and the annotated dataset covering 436 VerbNet classes.

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

🎓The University of Texas at AustinAustin · United States109 works · 56,942 citations🎓Nanyang Technological UniversitySingapore · Singapore156 works · 25,644 citations🎓University at Buffalo, State University of New YorkBuffalo · United States12 works · 318 citations

 Fields, topics and concepts

📚 Shelves 6The 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 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 works

Cite

Cite as Divyesh Pratap Singh, Dakshesh Gusain, Federica Bulgarelli, Alison Eisel Hendricks, John Beavers, Nathan M. Beers and Ifeoma Nwogu. 2026. A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research. Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.cdl-1.16

@inproceedings{singh2026scalable,
  title = {{A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research}},
  author = {Divyesh Pratap Singh, Dakshesh Gusain, Federica Bulgarelli, Alison Eisel Hendricks, John Beavers, Nathan M. Beers and Ifeoma Nwogu},
  booktitle = {Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)},
  year = {2026},
  doi = {10.18653/v1/2026.cdl-1.16},
  url = {https://doi.org/10.18653/v1/2026.cdl-1.16}
}

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