Learning Context-Dependent Mappings from Sentences to Logical Form

We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with 83.7% accuracy.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Luke S. Zettlemoyer, Michael Collins
• Keywords: ACL 2009, Computational Linguistics, Lambda-calculus Meaning Representations, Logical Forms, Salient Discourse Entities