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Data
New Dataset
nlg-eval-master
DMN+.py
Fact2seq_baseline.py
Fact2seq_w_attention_baseline.py
LICENSE
README.md
evaluation.py
model.py
result_DGN.csv
result_DGN_hyp.txt
result_DGN_ref.txt
static_memory_baseline.py

README.md

Project Title

This project is a PyTorch implementation of the ACL 2018 paper: Generating Fine-Grained Open Vocabulary Entity Type Descriptions

Prerequisites

Python 2.7+
PyTorch

Run

python <model_name>.py

This will complete the training, validation, and testing of the model. The results for the test data will be written to a file named "result_<model_name>.csv". For example, to run our model, use

python model.py

Evaluation

To run evaluation script, use

python evaluate.py result_<model_name>.csv

Authors

Citation

If you use this code, please cite our paper.

@inproceedings{Bhowmik2018EntityDescriptions,
  title = {Generating Fine-Grained Open Vocabulary Entity Type Descriptions},
  author = {Bhowmik, Rajarshi and {de Melo}, Gerard},
  booktitle = {Proceedings of ACL 2018},
  year = {2018},
  location = {Melbourne},
}

License

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