Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective

Publikation: Konferencebidrag - EJ publiceret i proceeding eller tidsskriftPaperForskningpeer review

Abstract

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, there has been a lack of common experimental standards compared to more established disciplines. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in DL into a single, widely-applicable methodology. Following these best practices is crucial to strengthening experimental evidence, improve reproducibility and enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.
OriginalsprogEngelsk
Publikationsdato29 apr. 2022
StatusUdgivet - 29 apr. 2022
BegivenhedML Evaluation Standards Workshop at ICLR 2022 -
Varighed: 29 apr. 2022 → …

Konference

KonferenceML Evaluation Standards Workshop at ICLR 2022
Periode29/04/2022 → …

Emneord

  • Deep Learning
  • Natural Language Processing
  • Experimental Standards
  • Reproducibility
  • Scientific Methodology

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