CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make use of the concept of weights in our design to analyze the transition of hidden states. At the same time, we also describe how the content adaptive gate handles the received words and alleviates the long-term dependence problem. As a result, the unit can improve the accuracy of the experiments, and the results show that CARU not only has better performance than GRU, but also produces faster training. Moreover, the proposed unit is general and can be applied to all RNN related neural network models.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Pages693-703
Number of pages11
ISBN (Print)9783030638290
DOIs
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12532 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
Country/TerritoryThailand
CityBangkok
Period18/11/2022/11/20

Keywords

  • Content-adaptive
  • Gate recurrent unit
  • Long-Short Term Memory
  • Natural Language Processing
  • Recurrent neural network

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