TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Pengcheng Yin, Graham Neubig


Abstract
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.
Anthology ID:
D18-2002
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–12
Language:
URL:
https://aclanthology.org/D18-2002
DOI:
10.18653/v1/D18-2002
Bibkey:
Cite (ACL):
Pengcheng Yin and Graham Neubig. 2018. TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 7–12, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (Yin & Neubig, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-2002.pdf
Code
 pcyin/tranX +  additional community code
Data
ATISCoNaLaCoNaLa-ExtDjangoWikiSQL