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.

该项目研究用于构建人工智能（AI）系统的新算法，该系统可以在人类参与的情况下学习提高其性能。最近许多人工智能的成功都是由庞大、昂贵且难以收集的数据集推动的。这就产生了一个深而窄的系统。该项目的目标是建立一种技术，使人工智能系统能够从与人的互动中学习。该项目侧重于与自然语言理解相关的关键应用：构建技术来理解单个句子的含义，并将这些含义整合到话语或对话的含义中。本文所追求的一个具体应用涉及从文本中提取生物医学知识，这将为帮助生物医学研究人员发展新的假设铺平道路。这项工作将资助来自STEM中代表性不足群体的学生，并鼓励研究生和本科阶段的跨学科教育。最后，这项工作不仅将通过科学论文向公众传播，还将通过社交媒体向国际传播，并通过访问当地的中学和高中向公众传播。自然语言处理（以及其他人工智能领域）通过在大型标记数据集（“语料库”）上训练监督学习系统取得了巨大的成功。不幸的是，除了非常特殊的问题，管理这样的语料库是不可行的。出现这种情况，要么是因为成本太高，要么是因为很难让人工标注人员就注释标准达成一致。该项目开发了可以从人类互动中学习的算法，而不是仅仅依赖于人类标记的数据。这些系统可以在下游性能监督的基础上不断改进其性能，通常有一个人在循环中。这项工作利用了结构化上下文强盗学习框架的最新发展，该框架提供了一种理论基础和计算效率高的方法，用于开发远程监督的新方法。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，潜在的模仿学习技术具有广泛的适用性，包括在递归神经网络模型中的新应用。为了帮助研究社区采用，该项目的代码和数据将以开源方式发布。