Collaboration or Corporate Capture? Quantifying NLP's Reliance on
  Industry Artifacts and Contributions
Will Aitken Mohamed Abdalla Karen Rudie Catherine Stinson
The advent of transformers, higher computational budgets, and big data has
engendered remarkable progress in Natural Language Processing (NLP). Impressive
performance of industry pre-trained models has garnered public attention in
recent years and made news headlines. That these are industry models is
noteworthy. Rarely, if ever, are academic institutes producing exciting new NLP
models. Using these models is critical for competing on NLP benchmarks and
correspondingly to stay relevant in NLP research. We surveyed 100 papers
published at EMNLP 2022 to determine whether this phenomenon constitutes a
reliance on industry for NLP publications.
  We find that there is indeed a substantial reliance. Citations of industry
artifacts and contributions across categories is at least three times greater
than industry publication rates per year. Quantifying this reliance does not
settle how we ought to interpret the results. We discuss two possible
perspectives in our discussion: 1) Is collaboration with industry still
collaboration in the absence of an alternative? Or 2) has free NLP inquiry been
captured by the motivations and research direction of private corporations?