Towards Reproducible Machine Learning Research in Natural Language Processing

While recent progress in the field of ML has been significant, the reproducibility of these cuttingedge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility (Hutson, 2018) . Despite proposals such as the Reproducibility Checklist (Pineau et al., 2020) and reproducibility criteria at several major conferences (NAACL, 2021; Dodge, 2020a; Beygelzimer et al., 2021) , 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. Target Audience and Prerequisites This tutorial targets senior researchers in academic institutions who want to include reproducibility initiatives in their coursework, and well as junior researchers who are interested in participating in reproducibility initiatives. The only prerequisite for this tutorial is a basic understanding of the scientific method.