  This paper describes a neural semantic parser that maps natural language
utterances onto logical forms which can be executed against a task-specific
environment, such as a knowledge base or a database, to produce a response. The
parser generates tree-structured logical forms with a transition-based approach
which combines a generic tree-generation algorithm with domain-general
operations defined by the logical language. The generation process is modeled
by structured recurrent neural networks, which provide a rich encoding of the
sentential context and generation history for making predictions. To tackle
mismatches between natural language and logical form tokens, various attention
mechanisms are explored. Finally, we consider different training settings for
the neural semantic parser, including a fully supervised training where
annotated logical forms are given, weakly-supervised training where denotations
are provided, and distant supervision where only unlabeled sentences and a
knowledge base are available. Experiments across a wide range of datasets
demonstrate the effectiveness of our parser.
