TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Open accessAbstract
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. 1 GenToken(sorted)
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- Authors
- Yin, P., & Neubig, G.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Published
- 2018
- Pages
- 7-12
- OpenAlex
- W2890867094
- Cited by
- 203
- References
- 25
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 21 · 2023: 44 · 2022: 23 · 2021: 48 · 2020: 24 · 2019: 13 · 2018: 3
- FWCI
- 8.95
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1815287)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- semantic parsing, code generation, natural language to code, information flow modeling
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