brightgems / SLCVAE

self labeling conditional variational auto encoder

Self Labeling Conditional Variational Auto Encoder

使用tensorflow实现论文Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder 论文链接paper

References

参考Knowledge-Guided CVAE for dialog generation的tensorflow实现代码.

论文模型如下：

Model

Dataset

• 对话数据集,同kgCVAE
• 自动回复聊天数据集

Prerequisites

• TensorFlow 1.12.0
• cuDNN 6
• Python 2.7
• Numpy
• NLTK
• You may need to pip install beeprint if the module is missing

Train

python kgcvae_swda.py

Test a existing model

Modify the TF flags at the top of kgcvae_swda.py as follows to run a existing model

forward_only: False -> True
test_path: set to the folder contains the model. E.g. runxxxx

Then you can run the model by:

python kgcvae_swda.py

The outputs will be printed to stdout and generated responses will be saved at test.txt in the test_path.