Transforming Multi-Conditioned Generation from Meaning Representation (RANLP 2021)
Requirements
- Pytorch 1.2+
- Python 3.5+
- Huggingface Transformer
- nltk library
Datasets & Evaluation
Train
For All dataset
python3 train.py
For sampling dataset
python3 train_sampling.py
Inference
Run inference.py to generate pred.txt with the trained model.
Evaluation
Refer to evaluation.ipynb file for e2e-metrics (BLEU, NIST, METEOR, ROUGE_L, CIDEr)
./e2e-metrics/measure_scores.py ./dataset/f_test.txt {prediction.txt}
For BERT score, edit the prediction file in the eval_BERTscore.py and
python3 eval_BERTscore.py
Citation
@inproceedings{lee-2021-transforming,
title = "Transforming Multi-Conditioned Generation from Meaning Representation",
author = "Lee, Joosung",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.92",
pages = "805--813"
}