| Autor: |
Ulmer, Dennis, Bassignana, Elisa, Müller-Eberstein, Max, Varab, Daniel, Zhang, Mike, van der Goot, Rob, Hardmeier, Christian, Plank, Barbara |
| Rok vydání: |
2022 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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