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.

介绍了一种新的 NLP 研究新范式 —— 翻译 NLP，利用该范式可以更好地将基础 NLP 和应用 NLP 联系起来，实现相互促进和交流，进一步推动 NLP 研究的发展。

翻译型 NLP：自然语言处理研究的新范式和通用原则

Translational NLP: A New Paradigm and General Principles for Natural  Language Processing Research

Evolution has produced a multi-scale mosaic of interacting adaptive units.
Innovations arise when perturbations push parts of the system away from stable
equilibria into new regimes where previously well-adapted solutions no longer
work. Here we explore the hypothesis that multi-agent systems sometimes display
intrinsic dynamics arising from competition and cooperation that provide a
naturally emergent curriculum, which we term an autocurriculum. The solution of
one social task often begets new social tasks, continually generating novel
challenges, and thereby promoting innovation. Under certain conditions these
challenges may become increasingly complex over time, demanding that agents
accumulate ever more innovations.

研究探讨了多代理系统的动态演化，提出了自学课程的假说，解释了社会任务解决时的新任务的产生以及挑战的不断增加。

自动课程与社交互动中创新的出现：多智能体研究宣言

Autocurricula and the Emergence of Innovation from Social Interaction: A  Manifesto for Multi-Agent Intelligence Research

Progress in science has advanced the development of human society across
history, with dramatic revolutions shaped by information theory, genetic
cloning, and artificial intelligence, among the many scientific achievements
produced in the 20th century. However, the way that science advances itself is
much less well-understood. In this work, we study the evolution of scientific
development over the past century by presenting an anatomy of 89 million
digitalized papers published between 1900 and 2015. We find that science has
benefited from the shift from individual work to collaborative effort, with
over 90% of the world-leading innovations generated by collaborations in this
century, nearly four times higher than they were in the 1900s. We discover that
rather than the frequent myopic- and self-referencing that was common in the
early 20th century, modern scientists instead tend to look for literature
further back and farther around. Finally, we also observe the globalization of
scientific development from 1900 to 2015, including 25-fold and 7-fold
increases in international collaborations and citations, respectively, as well
as a dramatic decline in the dominant accumulation of citations by the US, the
UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are
meant to serve as a starter for exploring the visionary ways in which science
has developed throughout the past century, generating insight into and an
impact upon the current scientific innovations and funding policies.

研究了 1900 年至 2015 年间的科学发展历程，发现合作研究已成为创新的主要来源，广泛参考文献，全球化的合作和引用关系大幅上升，美国，英国和德国科学累积的引用数量下降，这些发现有助于更好地探索科学发展的未来。