CAREER: Learning Scalable Models for Grounded Semantic Parsing

职业：学习基础语义解析的可扩展模型

• 批准号: 1252835
• 负责人: Luke Zettlemoyer
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252835&HistoricalAwards=false
• 关键词: CAREER; Learning; Scalable; Models; Grounded

One core challenge in natural language research is to do robust, wide coverage semantic analysis. Recently, there has been progress towards solving this problem by developing algorithms for learning semantic parsers that map sentences to rich, logical representations of their meaning. State-of-the-art approaches have reached the level where they can, with sufficient training data, be used to learn highly accurate parsers for many different natural languages on a number of benchmark problems. However, the general applicability of this work has been limited by the focus on somewhat idealized conditions, where the application domain is of limited size, sentences are analyzed in isolation, and there is an exclusive focus on database query applications.This CAREER project aims to build a framework for grounded semantic parsing that solves these challenges by reasoning about a sentence's possible meanings given its linguistic and situated context. This type of reasoning is necessary for extending existing learning approaches to fundamentally new applications, such as conversational understanding. However, it is also be crucial for the next generation of semantic parsers that learn from easily gathered data and scale to domains that are several orders of magnitude more complex than previously considered.The project will extend the PI's educational and outreach efforts, including the creation of freely shared online content for introductory and advanced semantics topics. It will also enable new initiatives by the PI to increase diversity in computer science study and research, by supporting efforts to motivate students through early exposure to exciting language understanding problems.

自然语言研究中的一个核心挑战是进行强大的，广泛的覆盖语义分析。 最近，通过开发学习语义解析器的算法，将句子映射到其含义丰富的逻辑表示形式来解决这个问题。 最先进的方法已经达到了可以使用足够的培训数据来学习许多基准问题的许多不同自然语言的高度准确解析器。但是，这项工作的一般适用性受到了对某种理想化条件的关注，在该条件下，应用程序域的大小有限，孤立地分析句子，并且在数据库查询应用程序上有独家的重点。该职业项目旨在建立一个固定语义分析的框架，以解决这些挑战，通过对句子的平均值来解决这些挑战，并以其可能的平均值来解决这些句子的范围，并以此为情境，并将其构建为“语言”。这种类型的推理对于将现有的学习方法扩展到从根本上的新应用（例如对话式理解）是必要的。 但是，对于下一代语义解析器而言，从易于收集的数据和规模中学习到比以前更为复杂的几个数量级的域也至关重要。该项目将扩展PI的教育和外展工作，包括创建自由共享的在线内容以创建介绍性和高级语言主题。这也将使PI的新举措能够通过支持激励学生尽早接触令人兴奋的语言理解问题的努力来增加计算机科学研究和研究的多样性。