# Automatic Annotation of Grammaticality in Child-Caregiver Conversations

arXiv: https://arxiv.org/abs/2403.14208 · HTML: https://mlsanity.com/paper/2403.14208/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [RuCoLA: Russian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2210.12814/) (2022)
- [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://mlsanity.com/paper/2109.12053/) (2021)
- [JCoLA: Japanese Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2309.12676/) (2023)

## Built on by (most cited first, 5 of 5)

- [Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models](https://mlsanity.com/paper/2505.23689/) (2025)
- [Evaluating Cognitive Age Alignment in Interactive AI Agents](https://mlsanity.com/paper/2605.17894/) (2026)
- [Child-directed speech facilitates production, not comprehension, in BabyLMs](https://mlsanity.com/paper/2606.01045/) (2026)
- [TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech](https://mlsanity.com/paper/2606.01820/) (2026)
- [Which Forms of Caregiver Feedback Support Grammar Learning? A Reinforcement-Learning Study of Child-Like Language Models](https://mlsanity.com/paper/2609.08576/) (2026)

## Abstract (arXiv)

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.