  Natural language processing (NLP) research combines the study of universal
principles, through basic science, with applied science targeting specific use
cases and settings. However, the process of exchange between basic NLP and
applications is often assumed to emerge naturally, resulting in many
innovations going unapplied and many important questions left unstudied. We
describe a new paradigm of Translational NLP, which aims to structure and
facilitate the processes by which basic and applied NLP research inform one
another. Translational NLP thus presents a third research paradigm, focused on
understanding the challenges posed by application needs and how these
challenges can drive innovation in basic science and technology design. We show
that many significant advances in NLP research have emerged from the
intersection of basic principles with application needs, and present a
conceptual framework outlining the stakeholders and key questions in
translational research. Our framework provides a roadmap for developing
Translational NLP as a dedicated research area, and identifies general
translational principles to facilitate exchange between basic and applied
research.
