Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

Understand

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.