mylv1222/git

Source code for ACL-IJCNLP 2021 Long paper: Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker.

Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker

Source code for ACL-IJCNLP 2021 Long paper: Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker.

Our code is based on Doc2EDAG.

0. Introduction

Document-level event extraction aims to extract events within a document. Different from sentence-level event extraction, the arguments of an event record may scatter across sentences, which requires a comprehensive understanding of the cross-sentence context. Besides, a document may express several correlated events simultaneously, and recognizing the interdependency among them is fundamental to successful extraction. To tackle the aforementioned two challenges, We propose a novel heterogeneous Graph-based Interaction Model with a Tracker (GIT). A graph-based interaction network is introduced to capture the global context for the scattered event arguments across sentences with different heterogeneous edges. We also decode event records with a Tracker module, which tracks the extracted event records, so that the interdependency among events is taken into consideration. Our approach delivers better results over the state-of-the-art methods, especially in cross-sentence events and multiple events scenarios.

• Architecture

• Overall Results

1. Package Description

GIT/
├─ dee/
    ├── __init__.py
    ├── base_task.py
    ├── dee_task.py
    ├── ner_task.py
    ├── dee_helper.py: data features constrcution and evaluation utils
    ├── dee_metric.py: data evaluation utils
    ├── config.py: process command arguments
    ├── dee_model.py: GIT model
    ├── ner_model.py
    ├── transformer.py: transformer module
    ├── utils.py: utils
├─ run_dee_task.py: the main entry
├─ train_multi.sh
├─ run_train.sh: script for training (including evaluation)
├─ run_eval.sh: script for evaluation
├─ Exps/: experiment outputs
├─ Data.zip
├─ Data: unzip Data.zip
├─ LICENSE
├─ README.md

2. Environments

• python (3.6.9)
• cuda (11.1)
• Ubuntu-18.0.4 (5.4.0-73-generic)

3. Dependencies

• numpy (1.19.5)
• torch (1.8.1+cu111)
• pytorch-pretrained-bert (0.4.0)
• dgl-cu111 (0.6.1)
• tensorboardX (2.2)

PS: The environments and dependencies listed here is different from what we use in our paper, so the results may be a bit different.

4. Preparation

• Unzip Data.zip and you can get an Data folder, where the training/dev/test data locate.

5. Training

>> bash run_train.sh

6. Evaluation

>> bash run_eval.sh

(The evaluation is also conducted after the training)

7. License

This project is licensed under the MIT License - see the LICENSE file for details.

8. Citation

If you use this work or code, please kindly cite the following paper:

@inproceedings{xu-etal-2021-git,
    title = "Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker",
    author = "Runxin Xu  and
      Tianyu Liu  and
      Lei Li and
      Baobao Chang",
    booktitle = "The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)",
    year = "2021",
    publisher = "Association for Computational Linguistics",
}