  We present Viola, an open-domain dialogue system for spoken conversation that
uses a topic-agnostic dialogue manager based on a simple generate-and-rank
approach. Leveraging recent advances of generative dialogue systems powered by
large language models, Viola fetches a batch of response candidates from
various neural dialogue models trained with different datasets and
knowledge-grounding inputs. Additional responses originating from
template-based generators are also considered, depending on the user's input
and detected entities. The hand-crafted generators build on a dynamic knowledge
graph injected with rich content that is crawled from the web and automatically
processed on a daily basis. Viola's response ranker is a fine-tuned polyencoder
that chooses the best response given the dialogue history. While dedicated
annotations for the polyencoder alone can indirectly steer it away from
choosing problematic responses, we add rule-based safety nets to detect neural
degeneration and a dedicated classifier to filter out offensive content. We
analyze conversations that Viola took part in for the Alexa Prize Socialbot
Grand Challenge 4 and discuss the strengths and weaknesses of our approach.
Lastly, we suggest future work with a focus on curating conversation data
specifcially for socialbots that will contribute towards a more robust
data-driven socialbot.
