GitHub - gdialektakis/Statistical-Dialogue-Systems-with-Adversarial-AutoEncoders

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_tutorialconfigs		_tutorialconfigs
_tutoriallogs		_tutoriallogs
autoencoder/adversarial_autoencoder/lib		autoencoder/adversarial_autoencoder/lib
belieftracking		belieftracking
config		config
curiosity		curiosity
evaluation		evaluation
ontology		ontology
policy		policy
resources		resources
scripts		scripts
semanticbelieftracking		semanticbelieftracking
semi		semi
semo		semo
tasks		tasks
tests		tests
topictracking		topictracking
usersimulator		usersimulator
utils		utils
Agent.py		Agent.py
CamRestaurants_dae_learning_rate_logs.csv		CamRestaurants_dae_learning_rate_logs.csv
DialogueServer.py		DialogueServer.py
LICENSE		LICENSE
Makefile		Makefile
README		README
Simulate.py		Simulate.py
Texthub.py		Texthub.py
__init__.py		__init__.py
cmd.log		cmd.log
conf.py		conf.py
createDocs.sh		createDocs.sh
pydial.py		pydial.py
requirements.txt		requirements.txt
results.txt		results.txt
testPydial		testPydial

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# Adversarial Learning in Statistical Dialogue Systems 
It's about representing Belief State Space with Adversarial Autoencoders in a Statistical Dialogue System. 
For that reason, Pydial framework is used.

-- For more information on how to download and run Pydial you can check the following link:
http://www.camdial.org/pydial/

-- Paper for Adversarial Autoencoders:
https://arxiv.org/abs/1511.05644

Thesis of this work:
http://purl.tuc.gr/dl/dias/61C5E296-DF97-49E6-9F34-7719775576DA