Paper: Learning Dependency-Based Compositional Semantics

ACL ID P11-1060
Title Learning Dependency-Based Compositional Semantics
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

Compositional question answering begins by mapping questions to logical forms, but train- ing a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. In this paper, we learn to map questions to answers via latent log- ical forms, which are induced automatically from question-answer pairs. In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and effi- cient evaluation of logical forms. On two stan- dard semantic parsing benchmarks (GEO and JOBS), our system obtains the highest pub- lished accuracies, despite requiring no anno- tated logical forms.