  We present TRANX, a transition-based neural semantic parser that maps natural
language (NL) utterances into formal meaning representations (MRs). TRANX uses
a transition system based on the abstract syntax description language for the
target MR, which gives it two major advantages: (1) it is highly accurate,
using information from the syntax of the target MR to constrain the output
space and model the information flow, and (2) it is highly generalizable, and
can easily be applied to new types of MR by just writing a new abstract syntax
description corresponding to the allowable structures in the MR. Experiments on
four different semantic parsing and code generation tasks show that our system
is generalizable, extensible, and effective, registering strong results
compared to existing neural semantic parsers.
