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Computer Science > Machine Learning

arXiv:2204.06251 (cs)
[Submitted on 13 Apr 2022 (v1), last revised 17 Oct 2022 (this version, v2)]

Title:Experimental Standards for Deep Learning in Natural Language Processing Research

Authors:Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank
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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, 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.
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2204.06251 [cs.LG]
  (or arXiv:2204.06251v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2204.06251

Submission history

From: Dennis Ulmer [view email]
[v1] Wed, 13 Apr 2022 08:42:52 UTC (5,367 KB)
[v2] Mon, 17 Oct 2022 12:55:33 UTC (12,777 KB)
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