A Survey on Dynamic Neural Networks for Natural Language Processing

9Citations
Citations of this article
51Readers
Mendeley users who have this article in their library.

Abstract

Effectively scaling large Transformer models is a main driver of recent advances in natural language processing. Dynamic neural networks, as an emerging research direction, are capable of scaling up neural networks with sub-linear increases in computation and time by dynamically adjusting their computational path based on the input. Dynamic neural networks could be a promising solution to the growing parameter numbers of pretrained language models, allowing both model pretraining with trillions of parameters and faster inference on mobile devices. In this survey, we summarize the progress of three types of dynamic neural networks in NLP: skimming, mixture of experts, and early exit. We also highlight current challenges in dynamic neural networks and directions for future research.

Cite

CITATION STYLE

APA

Xu, C., & McAuley, J. (2023). A Survey on Dynamic Neural Networks for Natural Language Processing. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023 (pp. 2325–2336). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-eacl.180

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free