Synthetic Data Made to Order: The Case of Parsing

Implementation of the paper "Synthetic Data Made to Order: The Case of Parsing" by Dingquan Wang and Jason Eisner. EMNLP 2018

Requirements

• Python3
• PyTorch 0.3

Run

• To permute a source treebank src.conllu in UD format towards a target language with only POS-tags tgt.txt in POS-spaced format (see data/fr.txt) and output to src~tgt.conllu:

     python src/main.py --src src.conllu --tgt tgt.txt --output src~tgt.conllu
  

  For convenience, --tgt could also take UD format input (endswith .conllu) which will ignore everything but the POS-tags.

• To pre-train a self-permutation model and use it as initialization:

     python src/main.py --task self_model  --src src.conllu --model $(pwd)/pre_model.pkl
     python src/main.py --src src.conllu --tgt tgt.txt --output src~tgt.conllu --pretrain $(pwd)/pre_model.pkl
  

• For more options, please use:

    python src/main.py --help
  

  the default hyperparameters are the ones used in the original paper

• We also release a sample data in data and a script permute.sh

Reference

@inproceedings{wang-eisner-2018-emnlp,
  author =      {Dingquan Wang and Jason Eisner},
  title =       {Synthetic Data Made to Order: The Case of Parsing},
  booktitle =   {Proceedings of the Conference on Empirical Methods in
                 Natural Language Processing (EMNLP)},
  year =        {2018},
  month =       nov,
  address =     {Brussels},
  url =         {http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-emnlp}
}