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CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractions

What it is about

The authors train a biaffine dependency parser (SuPar, with RoBERTa-large) on the gold part of UD-English-CHILDES, about 48k manually corrected sentences, and add a POS tagger and an utterance-level construction tagger. The question is whether a parser tuned to child and child-directed speech beats general-purpose English parsers. On the test split (9,591 sentences) the best model reaches LAS 92.51, against 85.19 for off-the-shelf Stanza (as read from Table 1). Pre-training on the silver Stanza data did worse than gold alone. A case study uses the construction tagger to follow the proportion of dative clitic and construction types across development.

Ideas

For Mova

Child speech is a register where Mova's English parser is weak, so the CHILDES results bear on en and gparse alike. The construction tagger is a rule layer that could sit in prag. Relevant chapters are languages/en/21-clause-structure, 38-dialogue-and-discourse-markers and 13-verbs (particle verbs). Measure first the LAS of en on the CHILDES test split against Stanza's number from this paper. UD-English-CHILDES is a training source in Mova's list; keep the test split held out.

Topics: Dependency parsing · Part-of-speech tagging

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.conll-main.23 · arXiv 2605.19718

@inproceedings{padovani2026cait,
  title = {{CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractions}},
  author = {Padovani, Francesca and Yang, Xiulin and Bunzeck, Bastian and Jumelet, Jaap and Matusevych, Yevgen and Schneider, Nathan and Bisazza, Arianna},
  booktitle = {Proceedings of the 30th Conference on Computational Natural Language Learning},
  year = {2026},
  doi = {10.18653/v1/2026.conll-main.23},
  eprint = {2605.19718},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2026.conll-main.23}
}

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