Code for the paper Optimal Neural Program Synthesis from Multimodal Specifications (Findings of EMNLP, 2021).
@inproceedings{ye2021optimal,
title={Optimal Neural Program Synthesis from Multimodal Specifications},
author = {Xi Ye, Qiaochu Chen, Isil Dillig, and Greg Durrett},
booktitle = {Findings of EMNLP},
year={2021}
}
- python==3.8
- pytorch==1.6.0
- JAVA 1.8.0
We've already attached trained checkpoint at checkpoints/streg/streg.enc.src100.field100.bin.
Preprocess data
python -c 'from datasets.streg.make_dataset import make_dataset;make_dataset()'
Run Optimal Synthesis
# <split>: the split (dev,testi, or teste) to evaluate on.
sh scripts/streg/synth.sh checkpoints/streg/streg.src100.field100.bin <split>
Train a Model
If you'd like to train a new ASN model, run the following command. The checkpoints will be stored at checkpoints/streg/
sh scripts/streg/train.sh
Run RobustFill
python -c 'from datasets.streg.make_deepcoder_data import make_exs_vocab;make_exs_vocab()'
sh scripts/streg/test_fill.sh checkpoints/streg/streg.robustfill.ioenc100.src100.field100.bin teste
Train RobustFill
sh scripts/streg/train_fill.sh
Part of the codes and system design are modified from TranX.
