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Neural reality of argument structure constructions

This repository contains the source code and data for our ACL 2022 paper: "Neural reality of argument structure constructions" by Bai Li, Zining Zhu, Guillaume Thomas, Frank Rudzicz, and Yang Xu.

Citation

If you use our work in your research, please cite:

Li, B., Zhu, Z., Thomas, G., Rudzicz, F., and Xu, Yang. 2022. Neural reality of argument structure constructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL).

@inproceedings{li2022neuralreality,
  author = "Li, Bai and Zhu, Zining and Thomas, Guillaume and Rudzicz, Frank and Xu, Yang",
  title = "Neural reality of argument structure constructions",
  booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)",
  publisher = "Association for Computational Linguistics",
  year = "2022",
}

Dependencies

The project was developed with the following library versions.

  • Python 3.9.9
  • numpy==1.22.0
  • pandas==1.3.5
  • scipy==1.7.3
  • scikit-learn==1.0.2
  • torch==1.10.1
  • transformers==4.15.0

Setup Instructions

  1. Clone this repo: git clone https://github.com/SPOClab-ca/neural-reality-constructions
  2. Download BNC Baby (4m word sample) from this link and extract into data/bnc/
  3. Run BNC preprocessing script: python scripts/process_bnc.py --bnc_dir=data/bnc/download/Texts --to=data/bnc.pkl
  4. (Optional) Run unit tests: PYTHONPATH=. python -m pytest test

Run sentence sorting (Case study 1)

PYTHONPATH=. python scripts/run_sentence_grouping.py --dataset=templates --model_name=roberta-base

Outputs four numbers: mean verb deviation, mean construction deviation, std of verb deviation, std of construction deviation.

Run Jabberwocky (Case study 2)

PYTHONPATH=. python scripts/run_jabberwocky.py --condition high-freq