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Discoparset-Supertag

This repository belongs to the Bachelor's thesis titled "Integrating Supertag Features into Neural Discontinuous Constituent Parsing". It can be downloaded here: https://mielczarek.de/?page_id=725

Install dependencies

Assuming you have a Python 3.10.9 environment.

Install disco-dop (https://github.com/andreasvc/disco-dop)

Install depCCG (2.0.3.2): (https://github.com/masashi-y/depccg) Note that the models (basic and rebank) currently need to be downloaded manually (see masashi-y/depccg#10). The links are in the README.md

After that run:

pip install -r requirements.txt

Generate datasets

DPTB:

git clone https://github.com/mcoavoux/multilingual_disco_data.git

and follow instructions in multilingual_disco_data/readme

You need to provide a copy of the DPTB (contact Kilian Evang).

CCGrebank:

Place unzipped CCGrebank folder into the project directory. Path can be changed via -ccg parameter when launching sfparser.py.

DepPTB:

Place unzipped DepPTB folder into project directory. Path can be changed via -depptb parameter when launching sfparser.py.

LCFRS:

Convert dptb7.export via LCFRS supertagger (https://github.com/truprecht/lcfrs-supertagger). Extract contents of .corpus-cache/dptb.3914-39832-1700-2416.bin-0-1-r.Vanilla.Vanilla.tar.gz into a folder named LCFRS in the project directory. Path can be changed via -lcfrs parameter when launching sfparser.py.

LTAG-spinal:

Download LTAG-spinal treebank from https://www.cis.upenn.edu/~xtag/spinal/ and place contents into a folder named LTAGspinalin the project directory. Path can be changed via -LTAGspinal parameter when launching sfparser.py.

Download models

Due to github's file size limitations, the models are stored externally. You can download them at https://drive.google.com/drive/folders/1Gqyq9H1TihH5D5Z0WWKHeQeDse-3wSfI?usp=sharing

I recommend using gdown to download from Google Drive: https://github.com/wkentaro/gdown

Reproduce result with pretrained models:

Each model has its own launch_experiments_<model>.sh script. In the script, comment out the line starting with python sfparser.py train. Then run in terminal:

sh launch_experiments_<model>.sh "dptb" "../pretrained_models/<model>" <device>

Use -1 for CPU and 0 for GPU (assuming you have only one GPU).

Reproduce results by retraining models:

Each model has its own launch_experiments_<model>.sh script. Run in terminal:

sh launch_experiments_<model>.sh "dptb" "../pretrained_models/<model> <device>

Use -1 for CPU and 0 for GPU (assuming you have only one GPU).

References:

This project extends the discoparset project created by

Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (Coavoux & Cohen, NAACL 2019)

Their code is available at https://gitlab.com/mcoavoux/discoparset