What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Social Sciences & Behavioral Studies, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

A new system has been developed to quantify the input children receive regarding filler-gap dependencies in language acquisition, a topic debated between innate knowledge and environmental evidence. This system automatically identifies three core filler-gap constructions in spoken English: matrix wh-questions, embedded wh-questions, and relative clauses, along with their specific extraction sites (subject, object, adjunct). It integrates constituency and dependency parsing to leverage their respective strengths for classification and site identification. Validated against human-annotated data, the system performs well across categories. Applied to 57 English CHILDES corpora, it characterizes children's filler-gap input and production trajectories, revealing construction-specific frequencies and extraction-site asymmetries. This fine-grained labeling supports future research in language acquisition and computational linguistics, exemplified by a case study using filtered corpus training for language models.

Key takeaway

For AI Scientists developing language models focused on human-like language acquisition, this system offers a precise method to analyze child-directed speech. You can use its fine-grained labels to filter training corpora, ensuring models learn specific grammatical structures like filler-gap dependencies more effectively. This approach could improve model performance on complex syntactic tasks by providing more targeted linguistic input during training.

Key insights

A new parsing system quantifies children's filler-gap dependency input and production trajectories in spoken English corpora.

Principles

• Combining constituency and dependency parsing enhances construction classification.
• Fine-grained linguistic labels enable targeted corpus training for LMs.

Method

The system identifies matrix wh-questions, embedded wh-questions, and relative clauses, then determines extraction sites (subject, object, adjunct) by combining constituency and dependency parsing.

In practice

• Apply the system to analyze specific linguistic input patterns in child language corpora.
• Use filtered corpus training with language models based on identified linguistic structures.

Topics

• Language Acquisition
• Filler-Gap Dependencies
• CHILDES Corpora
• Constituency Parsing
• Dependency Parsing
• Language Models

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.