Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
python run_model.py --model MODEL_NAME --datadir PATH_TO_DATADIR --config PATH_TO_CONFIG_FILE --mode [training/eval]
MODEL_NAME can be NKLM, DGN, Fact2SeqAttn, or PointerGenerator. E.g.
run_model.py --model NKLM --datadir Data/WikiFacts10k-OpenDomain --config Configs/NKLM-config.json --mode training
run_model.py --model PointerGenerator --datadir Data/WikiFacts10k-OpenDomain --config Configs/PointerGenerator-config.json --mode eval
NOTE: NKLM requires pre-trained TransE embeddings of entities and relations. To train the TransE model, run the following:
python TransE.py --datadir PATH_TO_DATADIR
We used the automatic evaluation script developed by Sharma et al., 2017. To run the evaluation script, run the following:
python evaluation.py <model_name>.csv
If you use this code, please cite our paper.
@inproceedings{BhowmikDeMelo2019EntityDescriptionsCopyModel,
title = {Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts},
author = {Bhowmik, Rajarshi and {de Melo}, Gerard},
booktitle = {Proceedings of The Web Conference 2019},
year = {2019},
location = {San Francisco},
}
This project is licensed under the MIT License - see the LICENSE file for details