RI: Small: Linguistic Semantics and Discourse from Leaky Distant Supervision

RI：小：来自泄漏远程监督的语言语义和话语

• 批准号: 1618193
• 负责人: Hal Daume
• 金额: $ 40.68万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618193&HistoricalAwards=false
• 关键词: RI; Small; Linguistic; Semantics; Discourse

This project studies novel algorithms for building artificial intelligence (AI) systems that can learn to improve their performance with a human in the loop. Many recent AI successes are driven by large, expensive and difficult-to-collect datasets. This yields systems that are deep, but narrow. The goal of this project is to build technology that will allow AI systems to learn from their interactions with people. The project focuses on key applications related to natural language understanding: building technology to understand the meanings of individual sentences, and integrate those meanings into the meaning of a discourse or dialog. One specific application pursued herein relates to extracting biomedical knowledge from text, which will pave the way to helping biomedical researchers develop novel hypotheses. The work will fund students from underrepresented groups in STEM, and encourage cross-disciplinary education at the graduate and undergraduate levels. Finally, the work will be communicated to the public not just with scientific papers, but internationally through social media and locally through visits to middle schools and high schools.Natural language processing (and other fields of artificial intelligence) have had enormous success by training supervised learning systems on large labeled datasets ("corpora"). Unfortunately, curating such corpora is infeasible except for very specific problems. This happens either because it is too expensive, or it is too difficult to get human labelers to agree on an annotation standard. Instead of relying solely on human labeled data, this project develops algorithms that can learn from human interaction. These systems can continually improve their performance based on downstream performance supervision, often with a human in the loop. This work leverages recent developments on the structured contextual bandits learning framework which provides a theoretically grounded and computationally efficient way in which to develop novel approaches to distant supervision. This resulting learning techniques will push advances in natural language understanding: semantic parsing and discourse interpretation. Furthermore, the underlying imitation learning technology is broadly applicable, including novel applications to recurrent neural network models. To aid adoption by the research community, code and data from this project will be released open source.

该项目研究了建立人工智能（AI）系统的新型算法，这些算法可以学会在循环中与人类的性能提高其性能。许多最近的AI成功都由大型，昂贵且难以收集的数据集驱动。这产生了深层但狭窄的系统。该项目的目的是建立技术，使AI系统可以从与人的互动中学习。该项目着重于与自然语言理解有关的关键应用：建立技术以了解单个句子的含义，并将这些含义纳入话语或对话的含义。 本文提出的一种特定应用与从文本中提取生物医学知识有关，这将为生物医学研究人员发展新的假设铺平道路。 这项工作将为STEM中代表性不足的团体的学生提供资金，并鼓励研究生和本科生的跨学科教育。最后，这项工作将不仅与科学论文交流，而且通过社交媒体在国际上传达，并通过访问中学和高中的访问。自然语言处理（以及其他人工智能领域）通过在大型标签数据集（“ Corpora”）上培训监督的学习系统，从而取得了巨大的成功。 不幸的是，除了非常具体的问题外，策划此类语料库是不可行的。发生这种情况是因为它太昂贵了，或者很难让人类标签者就注释标准达成共识。 该项目不仅依靠人类标记的数据，而是开发了可以从人类相互作用中学习的算法。 这些系统通常会根据下游的性能监督不断提高其性能，通常是在循环中的人。这项工作利用了结构化上下文匪徒学习框架的最新发展，该框架提供了一种理论上扎根且在计算上有效的方法，以开发新颖的方法来进行远距离监督。这项由此产生的学习技术将推动自然语言理解的进步：语义解析和话语解释。此外，基本的模仿学习技术广泛适用，包括对复发性神经网络模型的新应用。 为了帮助研究社区采用，该项目的代码和数据将被发布开源。