Workshop: Learning Hidden Linguistic Structure; January 3-7, 2019, New York New York

工作坊：学习隐藏的语言结构；

• 批准号: 1832737
• 负责人: Gaja Jarosz
• 金额: $ 1.79万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832737&HistoricalAwards=false
• 关键词: Workshop; Learning; Hidden; Linguistic; Structure

This workshop will bring together leading language researchers from two largely disjoint scientific communities: Linguistics and Natural Language Processing (NLP). The meeting will create a forum for intellectual exchange on linguistically oriented computational modeling and facilitate building of productive ties between the linguistics and NLP communities. To maximize the accessibility of the workshop to the broader linguistics community and to facilitate cross-disciplinary exchange, the workshop will take place in conjunction with the annual meeting of the Linguistic Society of America and will feature prominent invited speakers and panelists from the NLP community. Cross-disciplinary research that integrates principles of linguistics and findings from human language learning with cutting-edge computational methods from statistical machine learning promises to lead to scientific breakthroughs of mutual benefit to both research communities and to society more generally. Application of computational and mathematical modeling methods from NLP promises to enrich scientific understanding of the human language learning process and how it depends on the information present in children and adults' linguistic environments. The integration also has the potential to lead to new and improved language technologies, such as machine translation and automatic speech recognition systems that are playing an increasingly important role in modern society by facilitating communication between speakers of distinct languages, by increasing multilingual access to information and educational resources on the web, and by producing new tools and resources for people with speech, hearing, and language disabilities. In addition, the accessibility of the workshop to the broader linguistics community creates a pathway into STEM for women in linguistics that would not otherwise exist, providing educational, training and research connections with the NLP community.The theme for the workshop is "learning hidden linguistic structure", which is a topic chosen specifically because there are strong but largely separate research traditions approaching this fundamental learning problem in the two communities. Hidden structure is a common component of linguistic theories and of theories of human language learning, but much is still unknown about how such representations are learned by children from their linguistic input. On the other hand, NLP research has produced a wealth of computational techniques for modeling hidden structure and its learning, but these models rarely incorporate linguistic principles. Integrative research has the potential to lead to improvements in language technologies, especially in low-resource settings where success depends most on the capacity to generalize in linguistically appropriate ways from limited data. It also has potential to lead to scientific breakthroughs in understanding the sorts of computations and hidden representations that underlie human language learning. The workshop will host invited speakers who will present on the theme of "learning hidden linguistic structure" from interdisciplinary perspectives. To further increase ties between the linguistics and NLP communities, there will also be a special session with invited panelists on the topic of "What should linguists know about NLP? ". Finally, to facilitate the training of linguists in computational methods, the workshop will host two entry-level tutorials on machine learning methods in NLP.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本次研讨会将汇集来自两个基本上不相交的科学界的领先语言研究人员：语言学和自然语言处理（NLP）。会议将为面向语言的计算建模的知识交流创造一个论坛，并促进语言学和NLP社区之间建立富有成效的联系。为了最大限度地提高研讨会对更广泛的语言学社区的可访问性，并促进跨学科交流，研讨会将与美国语言学会年会同时举行，并将邀请来自NLP社区的着名演讲者和小组成员。将语言学原理和人类语言学习的发现与统计机器学习的尖端计算方法相结合的跨学科研究有望为研究界和社会带来互利的科学突破。NLP的计算和数学建模方法的应用有望丰富对人类语言学习过程的科学理解，以及它如何依赖于儿童和成人语言环境中存在的信息。这种整合还有可能导致新的和改进的语言技术，如机器翻译和自动语音识别系统，这些系统在现代社会中发挥着越来越重要的作用，促进了不同语言使用者之间的交流，增加了网上多语种信息和教育资源的获取，为有语言、听力、和语言障碍。此外，研讨会的可访问性，以更广泛的语言学界创造了一个途径，进入干的妇女在语言学，否则将不存在，提供教育，培训和研究与自然语言处理社区的联系。研讨会的主题是“学习隐藏的语言结构”，这是一个特别选择的主题，因为有强大的，但在很大程度上是独立的研究传统，接近这个基本的学习问题，在两个社区。隐藏结构是语言学理论和人类语言学习理论的一个共同组成部分，但关于儿童如何从他们的语言输入中学习这些表征仍然是未知的。另一方面，NLP研究已经产生了大量用于建模隐藏结构及其学习的计算技术，但这些模型很少包含语言学原则。综合研究有可能导致语言技术的改进，特别是在资源匮乏的环境中，成功与否主要取决于从有限的数据中以适当的语言方式进行概括的能力。它也有可能导致科学突破，理解人类语言学习背后的各种计算和隐藏的表征。研讨会将邀请演讲者，他们将从跨学科的角度介绍“学习隐藏的语言结构”的主题。为了进一步加强语言学和NLP社区之间的联系，还将举办一个特别会议，邀请小组成员讨论“语言学家应该知道NLP什么？".最后，为了促进语言学家在计算方法方面的培训，研讨会将举办两个关于NLP中机器学习方法的入门级教程。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。