Abstract

Computational models of language acquisition often face evaluation issues associated with unsupervised machine learning approaches. These acquisition models are typically meant to capture how children solve language acquisition tasks without relying on explicit feedback, making them similar to other unsupervised learning models. Evaluation issues include uncertainty about the exact form of the target linguistic knowledge, which is exacerbated by a lack of empirical evidence about children's knowledge at different stages of development. Put simply, a model's output may be good enough even if it does not match adult knowledge because children's output at various stages of development also may not match adult knowledge. However, it is not easy to determine what counts as “good enough” model output. We consider this problem using the case study of speech segmentation modeling, where the acquisition task is to segment a fluent stream of speech into useful units like words. We focus on a particular Bayesian segmentation strategy previously shown to perform well on English, and discuss several options for assessing whether a segmentation model's output is good enough, including cross-linguistic utility, the presence of reasonable errors, and downstream evaluation. Our findings highlight the utility of considering multiple metrics for segmentation success, which is likely also true for language acquisition modeling more generally.