Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
This addresses the challenge of creating more accurate natural language interfaces for databases, though it is an incremental improvement over existing methods.
The paper tackles the problem of mapping natural language sentences to lambda-calculus logical forms by developing a learning algorithm that induces a grammar and log-linear model from labeled training data. The method outperforms previous approaches in natural language interfaces to databases, achieving better performance on two benchmark domains.
This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.