On Variational Learning of Controllable Representations for Text without Supervision
Paper published in ICML 2020: arXiv
Download the GloVe embeddings from this link to data/. Then run:
python preprocess.py --data_name <data_name>
Data name can be either yelp or amazon.
Vanilla VAE:
python run_baseline.py --data_name <data_name>
Hyper-parameters can be set in baseline_config.py.
CP-VAE:
python run.py --data_name <data_name>
Hyper-parameters can be set in config.py.
Train the sentiment classifier:
python classifier.py --data_name <data_name>
Perform unsupervised style transfer:
Vanilla VAE:
python transfer_baseline.py --data_name <data_name> --load_path <path_to_checkpoint> --type <magnitude_type>
Magnitude type can be:
- 0, no change
- 1, move by one std
- 2, move by two std
- 3, move to extremum
CP-VAE:
python transfer.py --data_name <data_name> --load_path <path_to_checkpoint>
Calculate sentiment classification accuracy and BLEU score against the source sentence:
python evaluate.py --data_name <data_name> --target_path <path_to_target>
Visualization of NLL discrepency
Copy the nll statistics from the checkpoint folders to plot/ and then run:
python plot_nll.py
Topological Data Analysis
python tda.py --data_name <data_name> --load_path <path_to_checkpoint> --resolution <n>
python tda_baseline.py --data_name <data_name> --load_path <path_to_checkpoint> --resolution <n>
If you found this codebase or our work useful, please cite:
@InProceedings{xu2020variational,
author={Xu, Peng and Cheung, Jackie Chi Kit and Cao, Yanshuai},
title={On Variational Learning of Controllable Representations for Text without Supervision},
booktitle={The 37th International Conference on Machine Learning (ICML 2020)},
month={July},
year={2020},
publisher={PMLR},
}
Copyright (c) 2018-present, Royal Bank of Canada. All rights reserved. This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.