Evaluating the Cognitively-Related Productivity of a Universal Dependency Parser

Abstract

A key goal of cognitive computing is to correctly model human language. Recently, much has been made of the ability of deep neural nets trained on huge datasets to precisely parse sentences. But do these systems truly incorporate human knowledge of language? In this paper we apply a standard linguistic methodology, transformational analysis, to determine whether this claim is accurate. On this view, if a deep net parser operates properly on one kind of sentence, it should also work correctly on its transformed counterpart. Applying this to a standard set of statement-question transformed sentence pairs, we find that a state of the art neural network system does not replicate human behavior and makes numerous errors. We suggest that this kind of test is more relevant for highlighting what deep neural networks can and cannot do with respect to human language.