2021/04/15 by Denis Newman-Griffis, Jill Fain Lehman, Newman-Griffis, Denis +5
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2104.07874
openalex publication_date 2021/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Natural language processing (NLP) research combines the study of universal\nprinciples, through basic science, with applied science targeting specific use\ncases and settings. However, the process of exchange between basic NLP and\napplications is often assumed to emerge naturally, resulting in many\ninnovations going unapplied and many important questions left unstudied. We\ndescribe a new paradigm of Translational NLP, which aims to structure and\nfacilitate the processes by which basic and applied NLP research inform one\nanother. Translational NLP thus presents a third research paradigm, focused on\nunderstanding the challenges posed by application needs and how these\nchallenges can drive innovation in basic science and technology design. We show\nthat many significant advances in NLP research have emerged from the\nintersection of basic principles with application needs, and present a\nconceptual framework outlining the stakeholders and key questions in\ntranslational research. Our framework provides a roadmap for developing\nTranslational NLP as a dedicated research area, and identifies general\ntranslational principles to facilitate exchange between basic and applied\nresearch.\n