EAGER: Learning a High-Fidelity Semantic Parser

用普通语言与计算机交流是人工智能研究人员、教育、商业和政府企业以及每个使用计算机的人长期追求的目标。到目前为止，最令人印象深刻的系统依赖于数千名专业程序员对数千种专业“技能”的编码。诸如“恐怕我不能到会了”、“她设法及时注射了胰岛素”等普通评论被理解得不够好，无法得出诸如“我不会出席会议”、“她及时注射了胰岛素”等显而易见的结论。这个探索性的、渴望的项目向普通语言的机器理解迈出了一步，提供了一种在机器中表示语言内容的综合方法，并开发了一种机器学习技术，允许计算机将语言翻译成表示形式，从而做出上述类型的推理。这反过来又为改进需要一定程度的一般理解和推理的系统提供了直接的工具，例如对话系统、情感分析系统和从文本中提取所需知识的系统。语义解析器产生的高保真意义表示也为推导更深层次的意义提供了基础，使用了我们所知的语篇片段形成连贯段落的方式，并利用了关于词义和世界的一般知识。该项目团队由项目首席研究员、多名研究生和十几名本科生研究人员组成。该项目致力于以前所未有的逼真度推导出反映标准英语句子的语义类型结构的“无范围逻辑形式”(ULF)，不仅包括谓词，还包括数量、时态、情态、物化、谓词和句子修饰、比较结构等语义现象。因此，它远远超出了当前主流方法的表达范围，如抽象意义表征(AMR)。由于其类型连贯，ULF以比自然逻辑更全面的方式支持从文本中进行的前向话语推理，并且不需要对目标假设的知识进行确认或反驳。论证从句动词、反事实、问题和请求的推论提供了一个重要的概念证明。语义ULF解析器是由缓存转换解析器的监督学习产生的，与之前成功应用于AMR解析的解析器非常相似，但通过对类型一致的运算符-操作数组合进行优先排序而得到增强。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Communication with computers in ordinary language is a long-sought goal of AI researchers, educational, commercial, and government enterprises, and everyone who uses computers. The most impressive systems to date depend on coding of thousands of specialized "skills" by thousands of expert programmers. Ordinary comments such as "I'm afraid I won't make it to the meeting" and "She managed to get the insulin shot in time" are not understood well enough to draw obvious conclusions such as "I won't be at the meeting" and "She got the insulin shot in time". This exploratory EAGER project takes a step towards machine understanding of ordinary language, by providing a comprehensive way of representing the content of language in machines, and developing a machine learning technique that allows computers to translate language into that representation, and hence make the kinds of inferences mentioned. This in turn provides immediate tools for improving systems that require some degree of general understanding and inference, such as dialogue systems, sentiment analysis systems, and systems that extract desired knowledge from text. The high-fidelity representations of meaning produced by the semantic parser also provides a substrate for deriving deeper meanings, using what we know about the way discourse segments form coherent passages, and making use of general knowledge about word meanings and the world. The project team consists of a diverse group guided by the project principal investigators, several graduate-level and a dozen undergraduate-level researchers.This project focuses on deriving "unscoped logical forms" (ULFs) reflecting the semantic type structure of standard English sentences with unprecedented fidelity, covering not only predication but also quantification, tense, modality, reification, predicate and sentence modification, comparison structures, and other semantic phenomena. As such, it moves well beyond the expressive range of current mainstream approaches, such as Abstract Meaning Representation (AMR). Thanks to its type coherence, ULF supports forward discourse inferences from text in a more comprehensive way than Natural Logic, and without requiring knowledge of a target hypothesis to be confirmed or disconfirmed. Demonstrating inferences from clause-taking verbs, counterfactuals, questions, and requests provides an important proof of concept. The semantic ULF parser is produced by supervised learning of a cache transition parser, much like one previously applied successfully to AMR parsing, but enhanced by prioritizing type-consistent operator-operand combinations.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.