Constructing a Language From Scratch: Combining Bottom–Up and Top–Down Learning Processes in a Computational Model of Language Acquisition

We present a computational model that allows us to study the interplay of different processes involved in first language acquisition. We build on the assumption that language acquisition is usage-driven and assume that there are different processes in language acquisition operating at different levels. Bottom-up processing allows a learner to identify regularities in the linguistic input received, while top-down processing exploits prior experience and previous knowledge to guide choices made during bottom-up processing. To shed light on the interplay between top-down and bottom-up processing in language acquisition, we present a computational model of language acquisition that is based on bootstrapping mechanisms and is usage-based in that it relies on discovered regularities to segment speech into word-like units. Based on this initial segmentation, our model induces a construction grammar that in turn acts as a top-down prior that guides the segmentation of new sentences into words. We spell out in detail these processes and their interplay, showing that top-down processing increases both understanding performance and segmentation accuracy. Our model thus contributes to a better understanding of the interplay between bottom-up and top-down processes in first language acquisition and thus to a better understanding of the mechanisms and architecture involved in language acquisition.