Viola: A Topic Agnostic Generate-and-Rank Dialogue System
Hyundong Cho, Basel Shbita, Kartik Shenoy, Shuai Liu, Nikhil Patel,, Hitesh Pindikanti, Jennifer Lee, Jonathan May

TL;DR
Viola is a topic-agnostic, generate-and-rank dialogue system leveraging large language models, knowledge graphs, and safety mechanisms to produce contextually relevant and safe open-domain spoken conversations.
Contribution
It introduces Viola, a novel dialogue system combining neural, template-based, and knowledge-grounded responses with a fine-tuned ranker and safety filters, advancing open-domain conversational AI.
Findings
Viola participated in Alexa Prize Socialbot Challenge 4.
The system effectively integrates diverse response sources.
Analysis highlights strengths and areas for improvement.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
