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Communication Dans Un Congrès Année : 2024

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

Résumé

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.
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Dates et versions

hal-04512580 , version 1 (20-03-2024)

Identifiants

  • HAL Id : hal-04512580 , version 1

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Mitja Nikolaus, Abhishek Agrawal, Petros Kaklamanis, Alex Warstadt, Abdellah Fourtassi. Automatic Annotation of Grammaticality in Child-Caregiver Conversations. LREC-Coling 2024, May 2024, Turin, Italy. ⟨hal-04512580⟩
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