This repository contains the code and data for the CoNLL 2024 paper titled "Is Structure Dependence Shaped for Efficient Communication?: A Case Study on Coordination" (Kajikawa et al., 2024; 🏆Best Paper Award).
[Paper link] [arXiv]

For any questions, please contact:
kohei.kajikawa1223@gmail.com

We used Python==3.11.2.

Additionally, download the following repositories:

• artificial languages (White and Cotterell, 2021)
• rnng-pytorch (Noji and Oseki, 2021)
  • Note:
    You need to modify a few files in the rnng-pytorch repository as described in src/rnng-pytorch_/README.md. The modified versions of these files are in the src/rnng-pytorch_ directory. Please replace the corresponding files in the rnng-pytorch repository with these modified files.
    Ryo Yoshida (p.c.) provided me the code for calculating the logliks of each parse. Thanks Ryo!

The data and results we used are available as zip files at this google drive.

@inproceedings{kajikawa-etal-2024-structure,
    title = "Is Structure Dependence Shaped for Efficient Communication?: A Case Study on Coordination",
    author = "Kajikawa, Kohei  and
      Kubota, Yusuke  and
      Oseki, Yohei",
    editor = "Barak, Libby  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 28th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.conll-1.23/",
    doi = "10.18653/v1/2024.conll-1.23",
    pages = "291--302"
}