  Conversational agents have become an integral part of the general population
for simple task enabling situations. However, these systems are yet to have any
social impact on the diverse and minority population, for example, helping
people with neurological disorders, for example ALS, and people with speech,
language and social communication disorders. Language model technology can play
a huge role to help these users carry out daily communication and social
interactions. To enable this population, we build a dialog system that can be
controlled by users using cues or keywords. We build models that can suggest
relevant cues in the dialog response context which is used to control response
generation and can speed up communication. We also introduce a keyword loss to
lexically constrain the model output. We show both qualitatively and
quantitatively that our models can effectively induce the keyword into the
model response without degrading the quality of response. In the context of
usage of such systems for people with degenerative disorders, we present human
evaluation of our cue or keyword predictor and the controllable dialog system
and show that our models perform significantly better than models without
control. Our study shows that keyword control on end to end response generation
models is powerful and can enable and empower users with degenerative disorders
to carry out their day to day communication.
