Towards Reproducible Machine Learning Research in Natural Language Processing

Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, Robert Stojnic


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.
Anthology ID:
2022.acl-tutorials.2
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Luciana Benotti, Naoaki Okazaki, Yves Scherrer, Marcos Zampieri
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–11
Language:
URL:
https://aclanthology.org/2022.acl-tutorials.2
DOI:
10.18653/v1/2022.acl-tutorials.2
Bibkey:
Cite (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.
Cite (Informal):
Towards Reproducible Machine Learning Research in Natural Language Processing (Lucic et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-tutorials.2.pdf