CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractions
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Abstract (arXiv)
CHILDES is a paramount resource for language acquisition studies -- yet computational tools for analyzing its syntactic structure remain limited. Leveraging the recent release of the UD-English-CHILDES treebank with gold-standard Universal Dependencies (UD) annotations, we train a state-of-the-art dependency parser specifically tailored to CHILDES. The parser more accurately captures syntactic patterns in child-adult interactions, outperforming widely used off-the-shelf English parsers, including SpaCy and Stanza. Alongside the parser, we also release a Part-of-Speech tagger and an utterance-level construction tagger, which together form the open-source Syntactic Parsing Toolkit for Child-Adult InTeractions (CAIT). Through a detailed error analysis and a case study tracking the distribution of syntactic constructions across developmental time in CHILDES, we demonstrate the practical utility of the toolkit for large-scale, reproducible research on language acquisition.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 74 references that are in the map. First posted on arXiv 19 May 2026.
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14 of 74 references.
References · 14
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- 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
- 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 99
- 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
- 2025Do Construction Distributions Shape Formal Language Learning In German BabyLMs?cited by 11
- 2024UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependenciescited by 7
- 2025Domain Regeneration: How well do LLMs match syntactic properties of text domains?cited by 6
- 2022Data-driven Parsing Evaluation for Child-Parent Interactionscited by 3
- 2025UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactionscited by 3
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Citing papers per year: 2026: 1.
1 paper.