@inproceedings{ulmer-etal-2022-experimental,
    title = "Experimental Standards for Deep Learning in Natural Language Processing Research",
    author = {Ulmer, Dennis  and
      Bassignana, Elisa  and
      M{\"u}ller-Eberstein, Max  and
      Varab, Daniel  and
      Zhang, Mike  and
      van der Goot, Rob  and
      Hardmeier, Christian  and
      Plank, Barbara},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.196",
    pages = "2673--2692",
    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.",
}