@inproceedings{rabinovich-etal-2017-abstract,
    title = "Abstract Syntax Networks for Code Generation and Semantic Parsing",
    author = "Rabinovich, Maxim  and
      Stern, Mitchell  and
      Klein, Dan",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P17-1105/",
    doi = "10.18653/v1/P17-1105",
    pages = "1139--1149",
    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."
}