Proceedings of the Annual Meeting of the Cognitive Science Society

Dual language learners (DLLs) constitute a large portion of the population, but relatively little is known about the best ways in which to assess their vocabulary knowledge. Past research has used both conceptual vocabulary knowledge, assessing whether a child knows a word in either language, as well as total vocabulary knowledge, assessing what words a child knows in each language separately. The present work uses neural networks to predict specific word learning for individual Cantonese-English DLLs. As its input, The model utilizes word2vec embeddings that either represent children's' conceptual word knowledge or total word knowledge. We find that using total word knowledge results in higher predictive accuracy, suggesting that knowing what specific words DLLs know in each of their languages provides the most accurate picture of DLLs' vocabulary knowledge. The present work has many implications for both identification of at-risk individuals and the creation of learning materials for DLL populations.