Automatic Annotation of Grammaticality in Child-Caregiver Conversations

• URL: http://arxiv.org/abs/2403.14208v1
• Date: Thu, 21 Mar 2024 08:00:05 GMT
• Title: Automatic Annotation of Grammaticality in Child-Caregiver Conversations
• Authors: Mitja Nikolaus, Abhishek Agrawal, Petros Kaklamanis, Alex Warstadt, Abdellah Fourtassi,
• Abstract summary: This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale. We propose a coding scheme for context-dependent grammaticality and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels.
• Score: 7.493963534076502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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