RI: Medium: Deep Understanding: Integrating Neural and Symbolic Models of Meaning

RI：中：深度理解：整合意义的神经模型和符号模型

• 批准号: 1514268
• 负责人: Daniel Jurafsky
• 金额: $ 110万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514268&HistoricalAwards=false
• 关键词: RI; Medium; Deep; Understanding; Integrating

Natural language understanding, automatically computing the meaning of text, is key for allowing citizens to deal intelligently with the vast amount of digital information surrounding us, from the fine print on credit cards to science textbook chapters or online instructional material. The goal of this project is to develop systems that can build richer understandings of text than current systems. Humans have an incredible ability to integrate the structure of meaning --- how the meanings of sentences can be built up from the meanings of words --- with statistical knowledge about how words occur together with other words. Humans also effortlessly integrate meaning with 'reference', knowing which people or events in the world the text is talking about. But these tasks are quite difficult for computational systems. This project builds new computational models that integrate deep neural networks --- computational models with great power for representing word meaning in a statistical way --- with computational methods from logic and semantics. These new models allow word meanings to be combined together to build sentence meanings and also allow meanings to be linked with entities and events in the world. The resulting representations should help enable such societally important language understanding applications like question answering or tutorial software.This project develops compositional forms of deep learning that bridge between lexical and compositional semantics. This includes new kinds of embeddings that can be used to perform better meaning composition, computing for example that a student with a plaster cast is similar to an injured person just as earlier embeddings computed that injured is similar to hurt, and extending the virtues (such as lexical coverage) of embeddings to represent the denotations of logical predicates. Another focus is enriching models of meaning with models of reference, building entity-based models that can resolve coreference in texts to handle problems like bridging anaphora or verb and event coreference, with algorithms for entity-based coreference based on tensors that capture similarity of reference rather than similarity of lexical meaning. And it includes developing vector space lexicons that represent both natural language dependency tree fragments and logical fragments in a shared vector space, and representing meaning as general programs that can model the effects of events and processes on resources in the world. The new models are brought to bear on the end-to-end task of learning semantic parsers that map text to a semantic denotation.

自然语言理解，自动计算文本的含义，是允许公民智能地处理我们周围大量数字信息的关键，从信用卡上的小字到科学教科书章节或在线教学材料。这个项目的目标是开发出比现有系统更能理解文本的系统。人类有一种令人难以置信的能力，可以将意义结构——如何从单词的意义中构建句子的意义——与关于单词如何与其他单词一起出现的统计知识相结合。人类也毫不费力地将意义与“参考”结合起来，知道文本在谈论世界上的哪些人或事件。但是这些任务对于计算系统来说是相当困难的。该项目建立了新的计算模型，将深度神经网络（以统计方式表示单词含义的强大计算模型）与逻辑和语义的计算方法集成在一起。这些新模型允许将词义组合在一起来构建句子意义，也允许将意义与世界上的实体和事件联系起来。由此产生的表示应该有助于实现诸如问答或教程软件等具有社会重要性的语言理解应用程序。该项目开发了深度学习的组合形式，在词汇和组合语义之间架起了一座桥梁。这包括可用于执行更好的意义组合的新型嵌入，例如计算一个打石膏的学生与受伤的人相似，就像早期的嵌入计算受伤与受伤相似一样，并扩展嵌入的优点（如词汇覆盖）来表示逻辑谓词的外延。另一个重点是用指称模型丰富意义模型，建立基于实体的模型，可以解决文本中的共指问题，以处理桥接回指或动词和事件共指等问题，并使用基于张量的基于实体的共指算法，该算法捕获指称的相似性而不是词汇意义的相似性。它包括开发向量空间词典，在共享向量空间中表示自然语言依赖树片段和逻辑片段，并将意义表示为可以模拟事件和过程对世界资源的影响的通用程序。新模型用于学习将文本映射到语义表示的语义解析器的端到端任务。