  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, compared to more established disciplines, a lack of common
experimental standards remains an open challenge to the field at large.
Starting from fundamental scientific principles, we distill ongoing discussions
on experimental standards in NLP into a single, widely-applicable methodology.
Following these best practices is crucial to strengthen experimental evidence,
improve reproducibility and support scientific progress. These standards are
further collected in a public repository to help them transparently adapt to
future needs.
