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    <abstract>Acquiring telicity, which is the distinction between bounded (e.g., ate an apple) and unbounded (e.g., ate apples) events, requires first language (L1) learners to map surface-level and semantic cues to abstract event structures, but the computational trajectory of this mapping is not well understood. We introduce a Difference in Surprisal method that uses GPT2 token surprisal over paired temporal adverbial diagnostics (in an hour versus for an hour) to automatically label telicity across English CHILDES corpora, validated against expert linguist judgments. Using these labels, we train diagnostic logistic regression classifiers on 12 syntactic and lexical semantic features to compare how child speech and child-directed speech encode telicity. The two models diverge: the child model reaches near perfect accuracy through a single deterministic cue, the presence of a post-verbal determiner, while the adult model relies more heavily on verb class and other lexical semantic features, with the determiner cue neutralized. This trajectory supports Syntactic Bootstrapping: learners first exploit high-frequency structural cues to bootstrap, before developing fully compositional, verb-based event structures.</abstract>
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%0 Conference Proceedings
%T Modeling the Developmental Shift in Telicity Acquisition
%A Xia, Ellie
%A Kordjamshidi, Parisa
%A Ke, Alan Hezao
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F xia-etal-2026-modeling
%X Acquiring telicity, which is the distinction between bounded (e.g., ate an apple) and unbounded (e.g., ate apples) events, requires first language (L1) learners to map surface-level and semantic cues to abstract event structures, but the computational trajectory of this mapping is not well understood. We introduce a Difference in Surprisal method that uses GPT2 token surprisal over paired temporal adverbial diagnostics (in an hour versus for an hour) to automatically label telicity across English CHILDES corpora, validated against expert linguist judgments. Using these labels, we train diagnostic logistic regression classifiers on 12 syntactic and lexical semantic features to compare how child speech and child-directed speech encode telicity. The two models diverge: the child model reaches near perfect accuracy through a single deterministic cue, the presence of a post-verbal determiner, while the adult model relies more heavily on verb class and other lexical semantic features, with the determiner cue neutralized. This trajectory supports Syntactic Bootstrapping: learners first exploit high-frequency structural cues to bootstrap, before developing fully compositional, verb-based event structures.
%U https://aclanthology.org/2026.babylm-main.34/
%P 426-437

Markdown (Informal)

[Modeling the Developmental Shift in Telicity Acquisition](https://aclanthology.org/2026.babylm-main.34/) (Xia et al., BabyLM 2026)

• Modeling the Developmental Shift in Telicity Acquisition (Xia et al., BabyLM 2026)

ACL

• Ellie Xia, Parisa Kordjamshidi, and Alan Hezao Ke. 2026. Modeling the Developmental Shift in Telicity Acquisition. In Proceedings of the Second BabyLM Workshop, pages 426–437, Budapest, Hungary. Association for Computational Linguistics.