Code generation from natural language with less prior and more monolingual data (TAE)

Paper published in ACL 2021

install the requirments:

pip install -r requirements.txt

To train model on Django

python3 train.py --dataset_name django --save_dir CHECKPOINT_DIR --copy_bt --no_encoder_update --monolingual_ratio 1.0 --early_stopping

To evaluate the provided Django checkpoint:

python3 train.py --dataset_name django --save_dir pretrained_weights/django --copy_bt --no_encoder_update --monolingual_ratio 1.0 --early_stopping --just_evaluate --seed 2

To train model on CoNaLa

python3 train.py --dataset_name conala --save_dir CHECKPOINT_DIR --copy_bt --no_encoder_update --monolingual_ratio 0.5 --epochs 80

To evaluate the provided CoNaLa chceckpoint:

python3 train.py --dataset_name conala --save_dir pretrained_weights/conala --copy_bt --no_encoder_update --monolingual_ratio 0.5 --epochs 80 --just_evaluate --seed 4

Evaluation Results

Here are the evaluation numbers for the provided checkpoints:

Dataset	Results	Metric
Django	81.77	Exact Match Acc.
CoNaLa	33.41	Corpus BLEU