Codebase for our AAAI paper A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency.
- Python 3
- PyTorch
- tqdm
- sklearn
- pytorch-crf
To train the model, run:
bash train.sh model_name
Model checkpoints and logs will be saved to ./saved_models/model_name.
For the complete list of parameters see train.py.
To run evaluation on the test set, run:
python eval.py saved_models/model_name --dataset test
This will use the best_model.pt file by default. Use --model checkpoint_epoch_10.pt to specify a model checkpoint file.
The evaluation script will print results of different metrics. We use the macro-f1 from sklearn as our main metric to compare the models.
Reload a pretrained model and finetune it, run:
python train.py --load --model_file saved_models/model_name/best_model.pt --optim sgd --lr 0.001
CC BY-NC-SA 4.0.
If you use the code released in this repo, please cite our paper:
@inproceedings{veyseh2020joint,
title={A joint model for definition extraction with syntactic connection and semantic consistency},
author={Veyseh, Amir and Dernoncourt, Franck and Dou, Dejing and Nguyen, Thien},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={9098--9105},
year={2020}
}