RI: Small: Scalable Algorithms for Learning to Recover Logical Form from Natural Language

自然语言处理的一个关键目标是从自然语言句子到其潜在意义的形式表示的鲁棒映射。最近的工作通过学习语义解析器来解决这个问题，给出了与逻辑意义表示配对的句子。这个项目的目标是开发模型和学习算法来恢复词汇结构，在上下文中将句子映射到逻辑形式。这项工作的灵感来自词典的语言学理论，但直接受到当前最先进的学习算法所观察到的局限性的启发。本研究的核心假设是，一种新的用于词汇泛化的概率学习方法可以同时实现以下目标：(1)独立于语言的学习；(2)在分析自然的、未经编辑的文本时的鲁棒性；(3)需要减少数据注释的工作量，以一种计算效率高的方式扩展到大型学习问题。正在开发的方法引入了一种组合范畴语法（CCG），该方法经过修改，将词典中传统的显式词汇项列表替换为词汇项分布，从而允许在给定输入单词的可能语法和语义结构的构建中进行显著的泛化。以这种方式修改CCG词典大大增加了从可用训练数据进行泛化的可能性，而不会牺牲在已建立的语法形式中工作所带来的可伸缩性，而高效的学习和解析算法已经为此开发出来。这项工作将在算法层面和通过应用程序产生影响，包括为非技术用户提供数据库的高级自然语言接口。

A key aim in Natural Language Processing is to robustly map from natural language sentences to formal representations of their underlying meaning. Recent work has addressed this problem by learning semantic parsers given sentences paired with logical meaning representations. The goal of this project is to develop models and learning algorithms for recovering lexical structure, in the context mapping sentences to logical form. This work is inspired by linguistic theories of the lexicon, but directly motivated by the limitations observed in current, state-of-the-art learning algorithms.The central hypothesis is that a new probabilistic learning approach for lexical generalization can simultaneous achieve the goals of (1) language-independent learning, (2) robustness when analyzing natural, unedited text, and (3) requiring reduced data annotation effort, in a computationally efficient manner that will scale to large learning problems. The approach under development induces a Combinatory Categorial Grammar (CCG), that is modified to replace the traditional, explicit list of lexical items in the lexicon with a distribution over lexical items that allows for significant generalization in the construction of possible syntactic and semantic structures for given input words. Modifying the CCG lexicon in this manner greatly increases the potential to generalize from the available training data without sacrificing the scalability that comes from working within an established grammar formalism for which efficient learning and parsing algorithms have been developed. This work will have impact at the algorithmic level and through applications, including advanced natural language interfaces to databases for non-technical users.