Robert Stojnic


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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
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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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AxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas | Piotr Czapla | Pontus Stenetorp | Sebastian Ruder | Sebastian Riedel | Ross Taylor | Robert Stojnic
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub.