@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",
editor = "Benotti, Luciana and
Okazaki, Naoaki and
Scherrer, Yves and
Zampieri, Marcos",
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.",
}
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%0 Conference Proceedings
%T Towards Reproducible Machine Learning Research in Natural Language Processing
%A Lucic, Ana
%A Bleeker, Maurits
%A Bhargav, Samarth
%A Forde, Jessica
%A Sinha, Koustuv
%A Dodge, Jesse
%A Luccioni, Sasha
%A Stojnic, Robert
%Y Benotti, Luciana
%Y Okazaki, Naoaki
%Y Scherrer, Yves
%Y Zampieri, Marcos
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lucic-etal-2022-towards
%X 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.
%R 10.18653/v1/2022.acl-tutorials.2
%U https://aclanthology.org/2022.acl-tutorials.2
%U https://doi.org/10.18653/v1/2022.acl-tutorials.2
%P 7-11
Markdown (Informal)
[Towards Reproducible Machine Learning Research in Natural Language Processing](https://aclanthology.org/2022.acl-tutorials.2) (Lucic et al., ACL 2022)
ACL
- Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, and Robert Stojnic. 2022. Towards Reproducible Machine Learning Research in Natural Language Processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pages 7–11, Dublin, Ireland. Association for Computational Linguistics.