Tracking Child Language Development With Neural Network Language Models.

Area of Science:

• Computational Linguistics
• Developmental Psychology
• Artificial Intelligence

Background:

• Neural networks can infer syntactic information without explicit training.
• Assessing child language development typically relies on expert-designed, language-specific metrics.
• Existing methods include Mean Length of Utterance, Developmental Sentence Score, and Index of Productive Syntax.

Purpose of the Study:

• To evaluate a data-driven recurrent neural network model for tracking child language development.
• To compare the model's performance against established language assessment metrics.
• To explore the potential of data-driven approaches for multilingual and cross-lingual language assessment.

Main Methods:

• Utilized a recurrent neural network encoder on child language utterance transcripts from the CHILDES Database.
• Developed an evaluation methodology to compare the model's tracking of language development.
• Compared the model against Mean Length of Utterance, Developmental Sentence Score, and Index of Productive Syntax.

Main Results:

• The data-driven model successfully captured structural characteristics of child language utterances.
• The model demonstrated the ability to track language development over time, comparable to established metrics.
• The model's performance indicates its potential for data-driven language assessment.

Conclusions:

• A fully data-driven neural network model can effectively assess child language development from transcripts alone.
• This approach shows promise for multilingual and cross-lingual research in language acquisition.
• The findings support the use of AI in developing novel, adaptable language assessment tools.