NRT-DESE: Flexibility in Language Processes and Technology: Human- and Global-Scale

在人类和机器中学习语言，对全球技术、商业、教育、健康和国家安全具有深远的相关性。这一国家科学基金会研究培训(NRT)奖为马里兰大学学院园区的博士生准备了促进语言技术和语言学习的工具。该项目为学员提供了从语言学、计算机科学、心理学和神经科学的交叉培训中对学习模型的跨学科理解，并提供了使用多尺度语言数据的工具。该培训计划通过一个政策实习计划促进公众对科学的理解，该计划让受训人员与联邦机构和华盛顿地区的专业组织接触。此外，通过促进免费公共数字语言工具langscape的开发，它将为研究人员、公众、政府和非政府机构提供宝贵的资源，以发现有关世界语言的地理和语言信息。在人类和机器中灵活而高效地学习语言是这个NRT项目的研究重点。研究假设是，机器和人类学习的改善将来自于在多个尺度上更有效地使用训练数据的能力。通过跨学科小组方法，受训人员将探索有效使用语言数据，重点是数据对人类和机器学习的信息性。通过一系列培训活动，包括密集的暑期研究讲习班、与本科生和K-12学校的接触以及政策实习，学员将成为灵活的写作和演讲沟通者，并学习将他们的研究应用于不同的背景。

Language learning, in humans and machines, has far-reaching relevance to global technology, commerce, education, health, and national security. This National Science Foundation Research Traineeship (NRT) award prepares doctoral students at the University of Maryland, College Park with tools to advance language technology and language learning. The program provides trainees with an interdisciplinary understanding of learning models from cross-training in linguistics, computer science, and psychological and neural sciences, and with the tools to work with multi-scale language data. The training program contributes to the public understanding of science through a policy internship program that engages trainees with federal agencies and Washington-area professional organizations. Moreover, by contributing to the development of a free public digital linguistic tool, Langscape, it will provide a valuable resource for researchers, the public, the government, and nongovernmental agencies to discover geographical and linguistic information about languages of the world. Flexible and efficient language learning, in humans and machines, is the research focus of this NRT program. The research hypothesis is that improvements in learning in machines and in humans will come from the ability to use training data more efficiently at multiple scales. Through interdisciplinary team approaches, trainees will explore efficient use of language data, with a focus on the informativity of data to human and machine learning. Through a suite of training activities that includes intensive summer research workshops, engagement with undergraduates and K-12 schools, and policy internships, trainees will become flexible communicators in writing and speaking and also learn to apply their research to diverse contexts.