Modeling Children’s Early Linguistic Productivity Through the Automatic Discovery and Use of Lexically-based Frames

AbstractA central question for cognitive science is whether children’s linguistic productivity can be captured by item-based learning, or whether the learner must be guided by abstract, system-wide principles governed by innate constraints. Here, we present a computational model of early language acquisition which learns to discover and use lexically-based frames in a fully incremental, on-line fashion. The model is rooted in simple prediction- and recognition-based processes, subject to the same memory limitations as language learners. When exposed to English corpora of child-directed speech, the model is able learn developmentally plausible frames and use them to capture over 70% of the utterances produced by target children aged 2 to 5. Across a typologically diverse range of 29 languages, the model is able to capture over 68% of child utterances. Together, these findings suggest that much of children’s early linguistic productivity can be captured by item-based learning through computationally simple mechanisms.

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