Experimental Standards for Deep Learning in Natural Language Processing Research

MCML Authors

Barbara Plank

Prof. Dr.

Core PI

AI and Computational Linguistics

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 enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.

inproceedings UBM+22

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Findings @EMNLP 2022

Findings of the Conference on Empirical Methods in Natural Language Processing. Abu Dhabi, United Arab Emirates, Nov 07-11, 2022.

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Authors

D. Ulmer • E. Bassignana • M. Müller-Eberstein • D. Varab • M. Zhang • R. van der Goot • C. Hardmeier • B. Plank

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Links

DOI

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Research Area

 B2 | Natural Language Processing