End-to-End AMR coreference resolution

Code for the AMRcoref model in our ACL 2021 paper "End-to-End AMR coreference resolution".

1. Environment Setup

The code has been tested on Python 3.6 and PyTorch 1.6.0. All other dependencies are listed in requirements.txt.

Via conda:

conda create -n amrcoref python=3.6
source activate amrcoref
pip install -r requirements.txt

2. Data Preparation

Assuming that you're working on AMR 3.0 (LDC2020T02), unzip the corpus to data/AMR/LDC2020T02, and make sure it has the following structure:

(stog)$ tree data/AMR/LDC2020T02 -L 2
data/AMR/LDC2017T10
├── data
│   ├── alignments
│   ├── amrs
│   └── frames
│   └── multisentence
├── docs
│   ├── AMR-alignment-format.txt
│   ├── amr-guidelines-v1.3.pdf
│   ├── file.tbl
│   ├── frameset.dtd
│   ├── PropBank-unification-notes.txt
│   └── README.txt
└── index.html

Prepare corpus:

awk FNR!=1 data/amrs/unsplit/* > amr3.txt
awk FNR!=1 data/alignments/unsplit/* > amr-alignments.txt
python prepare_data.py

You can get the data like:

├── data
│   ├── align_unsplit
│   ├── amr_unsplit
│   └── xml_unsplit
├── amr3.txt
├── amr3_filtered
└── amr-alignments.txt

run the prepocessing codes for train/dev/test data:

python preprecess.py

If you want the preprocessed data directly, please send to us and let us know that you are authorized to use LDC2020T02 data.

If there are some bugs, please contact us.

3. Training

python train.py

4. Citation

If you find our code is useful, please cite:

@inproceedings{fu-etal-2021-end,
    title = "End-to-End {AMR} Corefencence Resolution",
    author = "Fu, Qiankun  and
      Song, Linfeng  and
      Du, Wenyu  and
      Zhang, Yue",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.324",
    doi = "10.18653/v1/2021.acl-long.324",
    pages = "4204--4214"
}