Automatic Annotation of Grammaticality in Child-Caregiver Conversations
<|reference_start|>Automatic Annotation of Grammaticality in Child-Caregiver Conversations: The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels.As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children's grammaticality shows a steady increase with age.This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.<|reference_end|>
@article{nikolaus2024automatic,
    title={Automatic Annotation of Grammaticality in Child-Caregiver Conversations},
    author={Mitja Nikolaus (ILCB, LPL, LIS, TALEP), Abhishek Agrawal (ILCB, LIS,
  TALEP), Petros Kaklamanis, Alex Warstadt (SED), Abdellah Fourtassi (ILCB,
  LIS, TALEP)},
    journal={LREC-Coling 2024, May 2024, Turin, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14208},
    primaryClass={cs.CL}
}