ADVERSARIAL MASK TRANSFORMER FOR SEQUENTIAL LEARNING

Hou Lio, Shang En Li, Jen Tzung Chien

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

2 Scopus citations

Abstract

Mask language model has been successfully developed to build a transformer for robust language understanding. The transformer-based language model has achieved excellent results in various downstream applications. However, typical mask language model is trained by predicting the randomly masked words and is used to transfer the knowledge from rich-resource pre-training task to low-resource downstream tasks. This study incorporates a rich contextual embedding from pre-trained model and strengthens the attention layers for sequence-to-sequence learning. In particular, an adversarial mask mechanism is presented to deal with the shortcoming of random mask and accordingly enhance the robustness in word prediction for language understanding. The adversarial mask language model is trained in accordance with a minimax optimization over the word prediction loss. The worst-case mask is estimated to build an optimal and robust language model. The experiments on two machine translation tasks show the merits of the adversarial mask transformer.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4178-4182
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

Keywords

  • Adversarial learning
  • mask language model
  • sequential learning
  • transformer

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