Modeling the Developmental Shift in Telicity Acquisition

2026-09-15

AI Summary

研究使用GPT2和逻辑回归分类器分析儿童与成人对终点性习得的差异，揭示了句法引导在语言学习中的作用。

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 as a scaffold to bootstrap, before developing fully compositional, verb-based event structures.

Problem

• telicity
• first language learners
• event structures
• computational trajectory
• syntactic bootstrapping

Innovation

• Difference in Surprisal
• GPT2
• Telicity Acquisition
• Syntactic Bootstrapping