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The advent of transformers, higher computational budgets, and big data hasengendered remarkable progress in Natural Language Processing (NLP). Impressiveperformance of industry pre-trained models has garnered public attention inrecent years and made news headlines. That these are industry models isnoteworthy. Rarely, if ever, are academic institutes producing exciting new NLPmodels. Using these models is critical for competing on NLP benchmarks andcorrespondingly to stay relevant in NLP research. We surveyed 100 paperspublished at EMNLP 2022 to determine whether this phenomenon constitutes areliance on industry for NLP publications.  We find that there is indeed a substantial reliance. Citations of industryartifacts and contributions across categories is at least three times greaterthan industry publication rates per year. Quantifying this reliance does notsettle how we ought to interpret the results. We discuss two possibleperspectives in our discussion: 1) Is collaboration with industry stillcollaboration in the absence of an alternative? Or 2) has free NLP inquiry beencaptured by the motivations and research direction of private corporations?

Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions