mlsanityData to 14 Oct 2026

NLP Reproducibility For All: Understanding Experiences of Beginners

arXiv 2305.16579 ↗First posted 26 May 2023cs.CL cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

As natural language processing (NLP) has recently seen an unprecedented level of excitement, and more people are eager to enter the field, it is unclear whether current research reproducibility efforts are sufficient for this group of beginners to apply the latest developments. To understand their needs, we conducted a study with 93 students in an introductory NLP course, where students reproduced the results of recent NLP papers. Surprisingly, we find that their programming skill and comprehension of research papers have a limited impact on their effort spent completing the exercise. Instead, we find accessibility efforts by research authors to be the key to success, including complete documentation, better coding practice, and easier access to data files. Going forward, we recommend that NLP researchers pay close attention to these simple aspects of open-sourcing their work, and use insights from beginners' feedback to provide actionable ideas on how to better support them.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 88 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

11 of 88 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Deep Reinforcement Learning that Matterscited by 754
  3. 2021Datasets: A Community Library for Natural Language Processingcited by 530
  4. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 322
  5. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 169
  6. 2020INFOTABS: Inference on Tables as Semi-structured Datacited by 86
  7. 2020Temporal Reasoning on Implicit Events from Distant Supervisioncited by 58
  8. 2021Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLPcited by 47
  9. 2021A Systematic Review of Reproducibility Research in Natural Language Processingcited by 34
  10. 2017Sustainable computational science: the ReScience initiativecited by 10
  11. 2014Case Studies and Challenges in Reproducibility in the Computational Sciencescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.