A syntactic neural model for parsing natural language to executable code
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lang
nn
.gitignore Initial commit Apr 20, 2017
README.md
astnode.py
code_gen.py
components.py
config.py
dataset.py
decoder.py
evaluation.py
interactive_mode.py
learner.py
main.py refact v1 Nov 26, 2016
model.py
parse.py
parse_hiro.py
run_interactive.sh
run_interactive_singlefile.sh
run_trained_model.sh
train.sh
util.py

README.md

NL2code

A syntactic neural model for parsing natural language to executable code paper.

Dataset and Trained Models

Get serialized datasets and trained models from here. Put models/ and data/ folders under the root directory of the project.

Usage

To train new model

. train.sh [hs|django]

To use trained model for decoding test sets

. run_trained_model.sh [hs|django]

Dependencies

  • Theano
  • vprof
  • NLTK 3.2.1
  • astor 0.6

Reference

@inproceedings{yin17acl,
    title = {A Syntactic Neural Model for General-Purpose Code Generation},
    author = {Pengcheng Yin and Graham Neubig},
    booktitle = {The 55th Annual Meeting of the Association for Computational Linguistics (ACL)},
    address = {Vancouver, Canada},
    month = {July},
    url = {https://arxiv.org/abs/1704.01696},
    year = {2017}
}