Conversational Contextual Cues: The Case of Personalization and History for Response Ranking

Understand

We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue.

• We specifically study the effect of incorporating different elements of the conversation.
• Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history.
• Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset.