@inproceedings{lucic-etal-2022-towards,
    title = "Towards Reproducible Machine Learning Research in Natural Language Processing",
    author = "Lucic, Ana  and
      Bleeker, Maurits  and
      Bhargav, Samarth  and
      Forde, Jessica  and
      Sinha, Koustuv  and
      Dodge, Jesse  and
      Luccioni, Sasha  and
      Stojnic, Robert",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-tutorials.2",
    doi = "10.18653/v1/2022.acl-tutorials.2",
    pages = "7--11",
    abstract = "While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs.",
}