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

Abstract

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.