Code for our ACL-2022 paper Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction.
$ conda env create -f environment.yml./preprocessing/./processed_data/./scripts/generate_data_ace05.sh and ./scripts/generate_data_ere.sh to generate training examples of different languages for X-Gear.
The generated training data will be saved in ./finetuned_data/.Run ./scripts/train_ace05.sh or ./scripts/train_ere.sh to train X-Gear. Alternatively, you can run the following command.
python ./xgear/train.py -c ./config/config_ace05_mT5copy-base_en.jsonThis trains X-Gear with mT5-base + copy mechanisim for ACE-05 English. The model will be saved in ./output/.
You can modify the arguments in the config file or replace the config file with other files in ./config/.
Run the following script to evaluate the performance for ACE-05 English, Arabic, and Chinese.
./scripts/eval_ace05.sh [model_path] [prediction_dir]If you want to test X-Gear with mT5-large, remember to modify the config file in ./scripts/eval_ace05.sh.
Run the following script to evaluate the performance for ERE English and Spanish.
./scripts/eval_ere.sh [model_path] [prediction_dir]If you want to test X-Gear with mT5-large, remember to modify the config file in ./scripts/eval_ere.sh.
We provide our pre-trained models and show their performances as follows.
| ACE-05 | en Arg-I | en Arg-C | ar Arg-I | ar Arg-C | zh Arg-I | zh Arg-C | |
|---|---|---|---|---|---|---|---|
| X-Gear-ace05-mT5-base+copy-en | 73.39 | 69.28 | 47.64 | 42.09 | 57.81 | 54.46 | |
| X-Gear-ace05-mT5-base+copy-ar | 33.87 | 27.17 | 72.97 | 66.92 | 31.14 | 28.84 | |
| X-Gear-ace05-mT5-base+copy-zh | 59.85 | 55.15 | 38.04 | 34.88 | 72.93 | 68.99 | |
| X-Gear-ace05-mT5-large+copy-en | 75.16 | 71.85 | 54.18 | 50.00 | 63.14 | 58.40 | |
| X-Gear-ace05-mT5-large+copy-ar | 38.81 | 34.57 | 73.49 | 67.75 | 39.26 | 36.13 | |
| X-Gear-ace05-mT5-large+copy-zh | 61.44 | 55.40 | 38.71 | 36.14 | 70.45 | 66.99 |
| ERE | en Arg-I | en Arg-C | es Arg-I | es Arg-C | |
|---|---|---|---|---|---|
| X-Gear-ere-mT5-base+copy-en | 78.26 | 71.55 | 64.31 | 58.70 | |
| X-Gear-ere-mT5-base+copy-es | 69.21 | 59.79 | 70.67 | 66.37 | |
| X-Gear-ere-mT5-large+copy-en | 78.10 | 73.04 | 64.82 | 60.35 | |
| X-Gear-ere-mT5-large+copy-es | 69.03 | 63.73 | 71.47 | 68.49 |
If you find that the code is useful in your research, please consider citing our paper.
@inproceedings{acl2022xgear,
author = {Kuan-Hao Huang and I-Hung Hsu and Premkumar Natarajan and Kai-Wei Chang and Nanyun Peng},
title = {Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2022},
}