  Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical form and computing the answer given a
structured database of facts. The core part of such a system is the semantic
parser that maps questions to logical forms. Semantic parsers are typically
trained from examples of questions annotated with their target logical forms,
but this type of annotation is expensive.
  Our goal is to learn a semantic parser from question-answer pairs instead,
where the logical form is modeled as a latent variable. Motivated by this
challenging learning problem, we develop a new semantic formalism,
dependency-based compositional semantics (DCS), which has favorable linguistic,
statistical, and computational properties. We define a log-linear distribution
over DCS logical forms and estimate the parameters using a simple procedure
that alternates between beam search and numerical optimization. On two standard
semantic parsing benchmarks, our system outperforms all existing
state-of-the-art systems, despite using no annotated logical forms.
