Code for "Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression".
Please cite:
@inproceedings{wu-etal-2023-estimating,
title = "Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression",
author = "Wu, Wen and Zhang, Chao and Woodland, Philip",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.873",
pages = "15681--15695",
}
PyTorch == 1.11
SpeechBrain == 0.5.13
data_preparation/msp-partition.py
-- prepare train/validation/test splitsdata_preparation/msp-label.py
-- prepare labelsdata_preparation/msp-data-json.py
-- prepare training scps
Example json file in msp-data/sample.json
python3 DEER_train.py DEER_config.yaml --output_folder='exp'
- Training log saved in exp/train_log.txt
- Model saved in exp/save
- Test predictions saved in exp/test_outcome-E{PLACEHOLDER}.npy
DEER_train.py
-- training script
DEER_config.yaml
-- training configuration
deep_evidential_emotion_regression.py
-- DEER loss and evidential layer
model.py
-- model class
utils.py
-- metrics, sampler, etc.
* Users are encouraged to experiment with different optimizers, schedulers, models, etc.
python3 scoring.py exp/test_outcome-E{PLACEHOLDER}.npy