Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child’s vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child’s current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child’s aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15–36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.

中文翻译：

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量化词汇知识在预测未来单词学习中的作用

给定关于孩子现在知道的单词的信息，我们能否预测孩子接下来要学习的单词？儿童词汇知识的不同表示是否会影响我们预测个别儿童词汇项习得的能力？过去的研究通常侧重于词汇增长的人口统计数据，而不是预测单个孩子接下来可能学习的单词。我们考虑使用神经网络方法来预测词汇习得。具体来说，我们研究如何最好地表示孩子当前的词汇，以便准确预测未来的学习。我们考虑的模型基于质量上不同的信息来源：关于孩子的描述性信息、孩子知道的特定单词、和表征，旨在捕捉孩子的聚合词汇知识。使用 15-36 个月儿童的纵向词汇数据，我们构建了神经网络模型来预测特定儿童在下个月可能会学习哪些单词。许多基于儿童特定词汇信息的模型优于仅包含儿童信息的模型，这表明儿童知道的单词会影响对未来语言学习的预测。这些模型提供了对当前词汇知识对未来词汇增长的作用的理解。许多基于儿童特定词汇信息的模型优于仅包含儿童信息的模型，这表明儿童知道的单词会影响对未来语言学习的预测。这些模型提供了对当前词汇知识对未来词汇增长的作用的理解。许多基于儿童特定词汇信息的模型优于仅包含儿童信息的模型，这表明儿童知道的单词会影响对未来语言学习的预测。这些模型提供了对当前词汇知识对未来词汇增长的作用的理解。