Awesome

Fast and Modularized Implementations of XCFGs

Update (08/06/2023): Add an evaluation of the transferability of VC-PCFG.

Update (12/12/2021): Add an implementation of VC-PCFG.

Update (03/10/2021): Fast and modularized implementations of XCFGs.

Model

This implementation reaches an average sentence-level F1 56%, slightly higher than Yoon's 55.2%. Notably, it takes only 25 minutes per epoch on a GeForce GTX 1080 Ti. Here is the report: An Empirical Study of Compound PCFGs.

Data

I am using the same data processing as Yoon. If you are looking for a unified data pipeline for WSJ, CTB, and SPMRL, I suggest you take a look at XCFG. It makes data creation easier. If you still find it annoying processing all the data from scratch, contact me and I can give you access to all the processed data (please make sure you have acquired licenses for these treebanks).

Mean sentence-level F1 numbers

Here is an overview of model performance on WSJ, CTB, and SPMRL. Find more details in the report.

Learning

Specify the model saving path M_ROOT and the data path D_ROOT before running. Check out XCFG if you are unsure about how to prepare data.

python train.py num_gpus=1 eval=False alias_root=$M_ROOT data.data_root=$D_ROOT \
    running.peep_rate=500 running.save_rate=1e9 running.save_epoch=True data.eval_samples=50000 \
    +model/pcfg=default \
    +optimizer=default \
    +data=default \
    +running=default

Parsing

Inference needs two more runninng parameters than learning: (1) M_NAME is the name of an experiment you have run and want to test and (2) M_FILE is the name of a model weight file from the experiment you have run.

python train.py num_gpus=1 eval=True alias_root=$M_ROOT data.data_root=$D_ROOT \
    model_name=$M_NAME model_file=$M_FILE data.eval_samples=50000 data.eval_name=english-test.json \
    +model/pcfg=default \
    +optimizer=default \
    +data=default \
    +running=default

Dependencies

It requires a tailored Torch-Struct.

git clone --branch beta https://github.com/zhaoyanpeng/cpcfg.git
cd cpcfg
virtualenv -p python3.7 ./pyenv/oops
source ./pyenv/oops/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
git clone --branch infer_pos_tag https://github.com/zhaoyanpeng/pytorch-struct.git
cd pytorch-struct
pip install -e .

Citation

If you use the fast implementation of C-PCFGs in your research or wish to refer to the results in the report, please use the following BibTeX entries.

@inproceedings{zhao-titov-2023-transferability,
    title = "On the Transferability of Visually Grounded {PCFGs}",
    author = "Zhao, Yanpeng  and Titov, Ivan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}

@inproceedings{zhao-titov-2021-empirical,
    title = "An Empirical Study of Compound {PCFG}s",
    author = "Zhao, Yanpeng and Titov, Ivan",
    booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.adaptnlp-1.17",
    pages = "166--171",
}

@inproceedings{zhao-titov-2020-visually,
    title = "Visually Grounded Compound {PCFG}s",
    author = "Zhao, Yanpeng  and Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.354",
    doi = "10.18653/v1/2020.emnlp-main.354",
    pages = "4369--4379",
}

Acknowledgements

This repo is developed based on C-PCFGs and Torch-Struct.

License

MIT