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.

该研究提出了一种基于转换的神经语义解析器 TRANX，它通过一个基于抽象语法描述语言的转换系统将自然语言表达转化为形式化的意义表示，从而具有高准确性和高可泛化性。实验表明，TRANX 相对于现有神经语义解析器，在处理四种不同的语义分析和代码生成任务时具有可扩展性和效率，并取得了强劲的结果。