This is an implementation of recurrent neural network grammars (RNNG) in PyTorch. RNNG is a neural constituency parser described in Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016). "Recurrent neural network grammars". In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 199–209). San Diego, California: Association for Computational Linguistics. Retrieved from http://www.aclweb.org/anthology/N16-1024. Dyer et al. also released their original DyNet implementation in https://github.com/clab/rnng. Please do check their work.
CAUTION: This implementation is still a work in progress. There is absolutely no guarantee of backward compatibility or even this is going to work. Do not use this for real work!
Make sure you have installed:
- Python 3.6
- PyTorch 0.2. Please follow the installation instruction here. Note that the latest PyTorch version is 0.3 and here we need 0.2.
Next, install all the requirements in requirements.txt
pip install -r requirements.txt
Then, install this package in development mode
pip install -e .
Run pytest from the project directory.
Run flake8 from the project directory. This will also run mypy to check type annotations, thanks to flake8-mypy.
MIT