This is the implementation of Zichao Yang's Improved Variational Autoencoders for Text Modeling using Dilated Convolutions with Kim's Character-Aware Neural Language Models embedding for tokens

Most of the implementations about the recurrent variational autoencoder are adapted from analvikingur/pytorch_RVAE

$ python train_word_embeddings.py

This script train word embeddings defined in Mikolov et al. Distributed Representations of Words and Phrases

--use-cuda

--num-iterations

--batch-size

--num-sample –– number of sampled from noise tokens

$ python train.py

--use-cuda

--num-iterations

--batch-size

--learning-rate

--dropout –– probability of units to be zeroed in decoder input

--use-trained –– use trained before model

$ python sample.py

--use-cuda

--num-sample