Cross-media Structured Common Space for Multimedia Event Extraction

Table of Contents

• Overview
• Requirements
• Data
• Training
• Testing
• Citation

Overview

The code for paper Connecting the Dots: Event Graph Schema Induction with Path Language Modeling.

Requirements

Python=3.7
PyTorch=1.4

Data

ACE (Text Event Extraction Data): We preprcoessed ACE following OneIE. Due to license reason, the ACE 2005 dataset is only accessible to those with LDC2006T06 license, please drop me an email (manling2@illinois.edu) showing your possession of the license for the processed data.

Training

Step 1. Prepare ACE data. Put the preprocessed ACE data under data/ace. The example of preprocessed ACE data is example.jsonin Data.zip.

Step 2. Generate paths.

cd data_utils/preprocessing/ace
python path_discover.py

Step 3. Generate training data for autoregressive language model and neighbor path classfication.

cd data_utils/preprocessing/ace
python path_tsv_vocab.py

Step 4. Train PathLM on two tasks,

sh path_xlnet_ft.sh

The variant of PathLM removing neighbor path classification can be trained as follows,

sh path_xlnet_ft_clm.sh

Testing

cd data_utils/preprocessing/ace
python evaluate_path.py

Citation

Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss. "Connecting the Dots: Event Graph Schema Induction with Path Language Modeling." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 684-695. 2020.

@inproceedings{li2020connecting,
  title={Connecting the Dots: Event Graph Schema Induction with Path Language Modeling},
  author={Li, Manling and Zeng, Qi and Lin, Ying and Cho, Kyunghyun and Ji, Heng and May, Jonathan and Chambers, Nathanael and Voss, Clare},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={684--695},
  year={2020}
}