Reproducing Hu, et. al., ICML 2017's "Toward Controlled Generation of Text" in PyTorch. This work is for University of Bonn's NLP Lab project on Winter Semester 2017/2018.

1. Python 3.5+
2. PyTorch 0.3
3. TorchText https://github.com/pytorch/text

1. Run python train_vae.py --save {--gpu}. This will create vae.bin. Essentially this is the base VAE as in Bowman, 2015 [2].
2. Run python train_discriminator --save {--gpu}. This will create ctextgen.bin. The discriminator is using Kim, 2014 [3] architecture and the training procedure is as in Hu, 2017 [1].
3. Run test.py --model {vae, ctextgen}.bin {--gpu} for basic evaluations, e.g. conditional generation and latent interpolation.

1. Only conditions the model with sentiment, i.e. no tense conditioning.
2. Entirely using SST dataset, which has only ~2800 sentences after filtering. This might not be enough and leads to overfitting. The base VAE in the original model by Hu, 2017 [1] is trained using larger dataset first.
3. Obviously most of the hyperparameters values are different.

1. Hu, Zhiting, et al. "Toward controlled generation of text." International Conference on Machine Learning. 2017. [pdf]
2. Bowman, Samuel R., et al. "Generating sentences from a continuous space." arXiv preprint arXiv:1511.06349 (2015). [pdf]
3. Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014). [pdf]