Learning Structural Edits via Incremental Tree Transformations

Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

1. Prepare Environment

We recommend using conda to manage the environment:

conda env create -n "structural_edits" -f structural_edits.yml
conda activate structural_edits

Install the punkt tokenizer:

python
>>> import nltk
>>> nltk.download('punkt')
>>> <ctrl-D>

2. Data

Please extract the datasets and vocabulary files by:

cd source_data
tar -xzvf githubedits.tar.gz

All necessary source data has been included as the following:

| --source_data
|       |-- githubedits
|           |-- githubedits.{train|train_20p|dev|test}.jsonl
|           |-- csharp_fixers.jsonl
|           |-- vocab.from_repo.{080910.freq10|edit}.json
|           |-- Syntax.xml
|           |-- configs
|               |-- ...(model config json files)

A sample file containing 20% of the GitHubEdits training data is included as source_data/githubedits/githubedits.train_20p.jsonl for running small experiments.

We have generated and included the vocabulary files as well. To create your own vocabulary, see edit_components/vocab.py.

Copyright: The original data were downloaded from Yin et al., (2019).

3. Experiments

See training and test scripts in scripts/githubedits/. Please configure the PYTHONPATH environment variable in line 6.

3.1 Training

For training, uncomment the desired setting in scripts/githubedits/train.sh and run:

bash scripts/githubedits/train.sh source_data/githubedits/configs/CONFIGURATION_FILE

where CONFIGURATION_FILE is the json file of your setting.

Supervised Learning

For example, if you want to train Graph2Edit + Sequence Edit Encoder on GitHubEdits's 20% sample data, please uncomment only line 21-25 in scripts/githubedits/train.sh and run:

bash scripts/githubedits/train.sh source_data/githubedits/configs/graph2iteredit.seq_edit_encoder.20p.json

(Note: when you run the experiment for the first time, you might need to wait for ~15 minutes for data preprocessing.)

Imitation Learning

To further train the model with PostRefine imitation learning, please replace FOLDER_OF_SUPERVISED_PRETRAINED_MODEL with your model dir in source_data/githubedits/configs/graph2iteredit.seq_edit_encoder.20p.postrefine.imitation.json. Uncomment only line 27-31 in scripts/githubedits/train.sh and run:

bash scripts/githubedits/train.sh source_data/githubedits/configs/graph2iteredit.seq_edit_encoder.20p.postrefine.imitation.json

3.2 Test

To test a trained model, first uncomment only the desired setting in scripts/githubedits/test.sh and replace work_dir with your model directory, and then run:

bash scripts/githubedits/test.sh

4. Reference

If you use our code and data, please cite our paper:

@inproceedings{yao2021learning,
    title={Learning Structural Edits via Incremental Tree Transformations},
    author={Ziyu Yao and Frank F. Xu and Pengcheng Yin and Huan Sun and Graham Neubig},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=v9hAX77--cZ}
}

Our implementation is adapted from TranX and Graph2Tree. We are grateful to the two work!

@inproceedings{yin18emnlpdemo,
    title = {{TRANX}: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation},
    author = {Pengcheng Yin and Graham Neubig},
    booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP) Demo Track},
    year = {2018}
}
@inproceedings{yin2018learning,
    title={Learning to Represent Edits},
    author={Pengcheng Yin and Graham Neubig and Miltiadis Allamanis and Marc Brockschmidt and Alexander L. Gaunt},
    booktitle={International Conference on Learning Representations},
    year={2019},
    url={https://openreview.net/forum?id=BJl6AjC5F7},
}