  Having engaging and informative conversations with users is the utmost goal
for open-domain conversational systems. Recent advances in transformer-based
language models and their applications to dialogue systems have succeeded to
generate fluent and human-like responses. However, they still lack control over
the generation process towards producing contentful responses and achieving
engaging conversations. To achieve this goal, we present \textbf{DiSCoL}
(\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational
\textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue
system that leverages conversational lines (briefly \textbf{convlines}) as
controllable and informative content-planning elements to guide the generation
model produce engaging and informative responses. Two primary modules in
DiSCoL's pipeline are conditional generators trained for 1) predicting relevant
and informative convlines for dialogue contexts and 2) generating high-quality
responses conditioned on the predicted convlines. Users can also change the
returned convlines to \textit{control} the direction of the conversations
towards topics that are more interesting for them. Through automatic and human
evaluations, we demonstrate the efficiency of the convlines in producing
engaging conversations.
