  This paper presents a novel framework for evaluating Neural Language Models'
linguistic abilities using a constructionist approach. Not only is the
usage-based model in line with the underlying stochastic philosophy of neural
architectures, but it also allows the linguist to keep meaning as a determinant
factor in the analysis. We outline the framework and present two possible
scenarios for its application.
