NLP Reproducibility For All: Understanding Experiences of Beginners

Shane Storks, Keunwoo Peter Yu, Ziqiao Ma, Joyce Chai

Main: Theme: Reality Check Main-poster Paper

Poster Session 1: Theme: Reality Check (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 10, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 10, Poster Session 1 (15:00-16:30 UTC)
Keywords: (non-)reproducibility
TLDR: 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 n...
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Abstract: 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.