Modelling children's grammar learning via caregiver feedback in natural conversations

Area of Science:

• Cognitive Science
• Developmental Psychology
• Computational Linguistics

Background:

• The role of negative evidence in language acquisition remains debated.
• Traditional methods struggle to isolate caregiver feedback from natural social interaction.
• Computational modeling offers a novel approach to study this problem.

Purpose of the Study:

• To investigate if caregiver feedback enhances grammatical learning beyond general input.
• To model the impact of specific feedback types, like clarification requests, on grammar acquisition.
• To introduce a flexible computational framework for evaluating various feedback mechanisms in language development.

Main Methods:

• Trained language models on child-directed language corpora.
• Fine-tuned models using reinforcement learning with caregiver-like feedback (clarification requests).
• Compared grammatical output and performance on grammar knowledge benchmarks.

Main Results:

• Fine-tuned models generated more grammatical utterances than baseline models.
• No significant improvement was observed on challenging grammar evaluation benchmarks.
• Demonstrated potential for improving benchmarks with additional feedback types.

Conclusions:

• Caregiver clarification requests can improve grammatical production in models.
• Current grammar evaluation benchmarks may not fully capture learning from feedback.
• The proposed computational framework facilitates broader research into social interaction and language development.