Proceedings of the Annual Meeting of the Cognitive Science Society

Computational models of morphology acquisition have played a central role in debates over the nature of morphological representations. The apparent success of recent artificial neural network architectures for morphological inflection in natural language processing has renewed this debate. However, the actual suitability of these advanced neural models as models of human morphology acquisition remains uncertain. We argue that much of this confusion stems from inconsistent methods of training and evaluation. In this work, we demonstrate that more careful data set creation and an evaluation combining quantitative analysis and comparison with human development will put the evaluation of neural models on firmer ground.