To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing

Abstract

NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity. Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure. We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time. We conclude by discussing shared visions, concerns, and hopes for the future of NLP. We hope that this study of our field's past and present can prompt informed discussion of our community's implicit norms and more deliberate action to consciously shape the future.

Accepted to EMNLP 2023

References in corpus (4)

• DyNet: The Dynamic Neural Network Toolkit
• Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
• The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
• Should attention be all we need? The epistemic and ethical implications of unification in machine learning

Cited by in corpus (1)

• We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields