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

自然语言理解，自动计算文本的含义，是让公民智能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。该项目的目标是开发能够比当前系统更丰富地理解文本的系统。人类有一种令人难以置信的能力来整合意义的结构--句子的意义是如何从单词的意义中建立起来的--以及单词如何与其他单词一起出现的统计知识。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。该项目构建了新的计算模型，将深度神经网络--具有以统计方式表示词义的强大功能的计算模型--与逻辑和语义的计算方法相结合。这些新的模型允许单词的意义被组合在一起，以建立句子的意义，也允许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这样的社会重要的语言理解应用程序，如问答或教程软件。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桥梁。这包括可以用来执行更好的意义合成的新型嵌入，例如，计算一个带石膏模型的学生与受伤的人相似，就像早期的嵌入计算受伤的人与受伤的人相似一样，并扩展嵌入的优点（如词汇覆盖）来表示逻辑谓词的外延。另一个重点是丰富的意义模型与参考模型，建立基于实体的模型，可以解决文本中的共指处理问题，如桥接回指或动词和事件的共指，基于实体的共指算法的基础上张量，捕捉相似的参考，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。 新的模型被带到承担端到端的任务，学习语义解析器，映射文本的语义表示。

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.