Abstract Syntax Networks for Code Generation and Semantic Parsing

Maxim Rabinovich, Mitchell Stern, Dan Klein


Abstract
Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.
Anthology ID:
P17-1105
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1139–1149
Language:
URL:
https://aclanthology.org/P17-1105
DOI:
10.18653/v1/P17-1105
Bibkey:
Cite (ACL):
Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017. Abstract Syntax Networks for Code Generation and Semantic Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1139–1149, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Abstract Syntax Networks for Code Generation and Semantic Parsing (Rabinovich et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1105.pdf
Video:
 https://aclanthology.org/P17-1105.mp4
Data
ATIS