A lively research field has recently emerged that uses experimental methods to probe the linguistic behavior of modern deep networks. While work in this tradition often reports intriguing results about the grammatical skills of deep nets, it is not clear what their implications for linguistic theorizing should be. As a consequence, linguistically-oriented deep net analysis has had very little impact on linguistics at large. In this chapter, I suggest that deep networks should be treated as theories making explicit predictions about the acceptability of linguistic utterances. I argue that, if we overcome some obstacles standing in the way of seriously pursuing this idea, we will gain a powerful new theoretical tool, complementary to mainstream algebraic approaches.
翻译:最近出现了一个活跃的研究领域,它利用实验方法来调查现代深层网络的语言行为。这个传统的工作经常报告深网的语法技能方面令人感兴趣的结果,但不清楚其对语言理论应产生什么影响。结果,以语言为导向的深层网络分析对一般语言影响不大。在本章中,我建议将深层网络视为对语言话语的可接受性作出明确预测的理论。我争辩说,如果我们克服了在认真追求这一理念过程中遇到的一些障碍,我们将获得一个强大的新理论工具,作为对主流代数法的补充。