CAREER: Learning Scalable Models for Grounded Semantic Parsing

自然语言研究的一个核心挑战是进行鲁棒的、广泛覆盖的语义分析。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的丰富逻辑表示，在解决这个问题方面取得了进展。最先进的方法已经达到了这样的水平：只要有足够的训练数据，它们就可以在许多基准问题上学习针对许多不同自然语言的高度精确的解析器。然而，这项工作的一般适用性受到一些理想化条件的限制，在这些条件下，应用程序领域的大小有限，句子是孤立地分析的，并且只关注数据库查询应用程序。这个CAREER项目旨在建立一个基于语义分析的框架，通过在给定的语言和情境下推理句子的可能含义来解决这些挑战。这种类型的推理对于将现有的学习方法扩展到全新的应用（例如会话理解）是必要的。然而，对于下一代语义解析器来说，这也是至关重要的，下一代语义解析器可以从容易收集的数据中学习，并扩展到比以前考虑的复杂几个数量级的领域。该项目将扩展PI的教育和推广工作，包括为入门和高级语义主题创建免费共享的在线内容。它还将使PI的新举措能够增加计算机科学学习和研究的多样性，支持通过早期接触令人兴奋的语言理解问题来激励学生的努力。

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.