TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
This work addresses the need for accurate and generalizable semantic parsers for natural language processing and code generation, though it appears incremental as it builds on existing transition-based methods with syntax constraints.
The paper tackles the problem of mapping natural language to formal meaning representations or code by introducing TRANX, a transition-based neural semantic parser that leverages abstract syntax descriptions, achieving strong results across four semantic parsing and code generation tasks.
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.