24th EANN 2023, 14 - 17 June 2023, León, Spain

A Multi-Relationship Language Acquisition Model for Predicting Child Vocabulary Growth

Andrew Roxburgh, Floriana Grasso, Terry R Payne


  If we can predict the words a child is likely to learn next, it may lay the foundations for developing a tool to assist child language acquisition, especially for children experiencing language delay. Previous studies have demonstrated vocabulary predictions using neural network techniques and graph models; however, individually these models do not fully capture the complexities of language learning in infants. In this paper, we describe a multi-relationship-layer predictive model, based on a graph neural network. Our model combines vocabulary development over time with quantified connections between words calculated from fifteen different norms, incorporating an ensemble output stage to combine the predictions from each layer. We present results from each relationship layer and the most effective ensemble arrangement.  

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