| dc.description.abstract | In the field of open-domain dialog systems, latent variable based models have shown to
deliver promising performances. However, response generation in these models cannot be
explicitly controlled. Therefore, researchers have come up with SPHRED, inspired by a latent
variable model called VHRED. SPHRED addresses the problem and controls generation by
generating responses based on attributes like level of genericness, or different sentiment modes,
or any attribute defined in accordance with specific contexts. In addition, SPHRED separately
models dialog states for both speakers to reflect personal features. The researchers tested
SPHRED in two different scenarios, where the attribute refers to genericness and to sentiment
states.
This thesis focuses on building SPHRED model in TensorFlow; testing its performance
to verify its capabilities on Ubuntu dataset [1] in terms of global structure capturing, response
generation control, and respective communication style replication; and addressing problems
faced by the model, then suggesting improvements to overcome them | en_US |
