Oral (Contributed Talk)
in
Workshop: Setting up ML Evaluation Standards to Accelerate Progress

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

Dennis Ulmer ⋅ Elisa Bassignana ⋅ Max Müller-Eberstein ⋅ Daniel Varab ⋅ Mike Zhang ⋅ Christian Hardmeier ⋅ Barbara Plank

2022 Oral (Contributed Talk)
in
Workshop: Setting up ML Evaluation Standards to Accelerate Progress

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.

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