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.

通过自动标注工具对上千个关于儿童和照顾者对话的语料进行了上下文相关语法的编码和标注，并通过训练和评估一系列基于 Transformer 的 NLP 模型表明，在儿童语法中，细调的 Transformer 模型表现最佳，达到了人类间标注一致性水平，从而在研究儿童语言习得方面应用了现有最先进的 NLP 方法。