@inproceedings{singh-etal-2026-scalable,
title = "A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research",
author = "Singh, Divyesh Pratap and
Gusain, Dakshesh and
Bulgarelli, Federica and
Hendricks, Alison Eisel and
Beavers, John and
Beers, Nathan M. and
Nwogu, Ifeoma",
editor = "Ma, Martin Ziqiao and
Liu, Emmy and
Liu, Jing and
Chang, Tyler A. and
Fourtassi, Abdellah and
Warstadt, Alex and
Hahn, Michael and
Sun, Weiwei and
Shi, Freda",
booktitle = "Proceedings of the 1st Workshop on Computational Developmental Linguistics ({CDL})",
month = jul,
year = "2026",
address = "Grand Hyatt Manchester San Diego, 1 Market Pl, San Diego, CA 92101",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.cdl-1.16/",
pages = "117--128",
ISBN = "979-8-89176-428-6",
abstract = "Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner and result classification are not currently available. We present a computational approach for identifying manner and result verbs in sentence context. Using linguistically informed prompts, we generate sentence-level annotations with large language models over data drawn from MASC and InterCorp, extending coverage from previously annotated portions of VerbNet to 436 classes. We then train a RoBERTa-based classifier on these annotations and evaluate it on three held-out gold-standard datasets, including previously annotated items and a new expert-annotated set. Across these evaluations, the model shows promising performance, with average accuracy up to 89.6{\%}. We present this work as a scalable measurement tool that can support future research on verb semantics in developmental and other language datasets, while noting that further validation is needed for borderline cases, mixed manner/result verbs, and downstream developmental applications."
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<abstract>Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner and result classification are not currently available. We present a computational approach for identifying manner and result verbs in sentence context. Using linguistically informed prompts, we generate sentence-level annotations with large language models over data drawn from MASC and InterCorp, extending coverage from previously annotated portions of VerbNet to 436 classes. We then train a RoBERTa-based classifier on these annotations and evaluate it on three held-out gold-standard datasets, including previously annotated items and a new expert-annotated set. Across these evaluations, the model shows promising performance, with average accuracy up to 89.6%. We present this work as a scalable measurement tool that can support future research on verb semantics in developmental and other language datasets, while noting that further validation is needed for borderline cases, mixed manner/result verbs, and downstream developmental applications.</abstract>
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%0 Conference Proceedings
%T A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research
%A Singh, Divyesh Pratap
%A Gusain, Dakshesh
%A Bulgarelli, Federica
%A Hendricks, Alison Eisel
%A Beavers, John
%A Beers, Nathan M.
%A Nwogu, Ifeoma
%Y Ma, Martin Ziqiao
%Y Liu, Emmy
%Y Liu, Jing
%Y Chang, Tyler A.
%Y Fourtassi, Abdellah
%Y Warstadt, Alex
%Y Hahn, Michael
%Y Sun, Weiwei
%Y Shi, Freda
%S Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)
%D 2026
%8 July
%I Association for Computational Linguistics
%C Grand Hyatt Manchester San Diego, 1 Market Pl, San Diego, CA 92101
%@ 979-8-89176-428-6
%F singh-etal-2026-scalable
%X Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner and result classification are not currently available. We present a computational approach for identifying manner and result verbs in sentence context. Using linguistically informed prompts, we generate sentence-level annotations with large language models over data drawn from MASC and InterCorp, extending coverage from previously annotated portions of VerbNet to 436 classes. We then train a RoBERTa-based classifier on these annotations and evaluate it on three held-out gold-standard datasets, including previously annotated items and a new expert-annotated set. Across these evaluations, the model shows promising performance, with average accuracy up to 89.6%. We present this work as a scalable measurement tool that can support future research on verb semantics in developmental and other language datasets, while noting that further validation is needed for borderline cases, mixed manner/result verbs, and downstream developmental applications.
%U https://aclanthology.org/2026.cdl-1.16/
%P 117-128
Markdown (Informal)
[A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research](https://aclanthology.org/2026.cdl-1.16/) (Singh et al., CDL 2026)
ACL
- 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. In Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL), pages 117–128, Grand Hyatt Manchester San Diego, 1 Market Pl, San Diego, CA 92101. Association for Computational Linguistics.