On the proper role of linguistically-oriented deep net analysis in linguistic theorizing
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Entity-based Neural Local Coherence Modeling
In this paper, we propose an entity-based neural local coherence model which is linguistically more sound than previously proposed neural coherence models. Recent neural coherence models encode the input document using l…
Entity-based Neural Local Coherence Modeling
In this paper, we propose an entity-based neural local coherence model which is linguistically more sound than previously proposed neural coherence models. Recent neural coherence models encode the input document using l…
How does the pre-training objective affect what large language models learn about linguistic properties?
Several pre-training objectives, such as masked language modeling (MLM), have been proposed to pre-train language models (e.g. BERT) with the aim of learning better language representations. However, to the best of our k…
Language ModelingLanguage ModellingMasked Language ModelingHow does the pre-training objective affect what large language models learn about linguistic properties?
Several pre-training objectives, such as masked language modeling (MLM), have been proposed to pre-train language models (e.g. BERT) with the aim of learning better language representations. However, to the best of our k…
Language ModelingLanguage ModellingMasked Language ModelingExplaining non-linear Classifier Decisions within Kernel-based Deep Architectures
Nonlinear methods such as deep neural networks achieve state-of-the-art performances in several semantic NLP tasks. However epistemologically transparent decisions are not provided as for the limited interpretability of …
General ClassificationImage ClassificationQuestion AnsweringSemantic Role Labeling