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Building open domain conversational systems that allow users to have engagingconversations on topics of their choice is a challenging task. Alexa Prize waslaunched in 2016 to tackle the problem of achieving natural, sustained,coherent and engaging open-domain dialogs. In the second iteration of thecompetition in 2018, university teams advanced the state of the art by usingcontext in dialog models, leveraging knowledge graphs for languageunderstanding, handling complex utterances, building statistical andhierarchical dialog managers, and leveraging model-driven signals from userresponses. The 2018 competition also included the provision of a suite of toolsand models to the competitors including the CoBot (conversational bot) toolkit,topic and dialog act detection models, conversation evaluators, and a sensitivecontent detection model so that the competing teams could focus on buildingknowledge-rich, coherent and engaging multi-turn dialog systems. This paperoutlines the advances developed by the university teams as well as the AlexaPrize team to achieve the common goal of advancing the science ofConversational AI. We address several key open-ended problems such asconversational speech recognition, open domain natural language understanding,commonsense reasoning, statistical dialog management, and dialog evaluation.These collaborative efforts have driven improved experiences by Alexa users toan average rating of 3.61, the median duration of 2 mins 18 seconds, andaverage turns to 14.6, increases of 14%, 92%, 54% respectively since the launchof the 2018 competition. For conversational speech recognition, we haveimproved our relative Word Error Rate by 55% and our relative Entity Error Rateby 34% since the launch of the Alexa Prize. Socialbots improved in qualitysignificantly more rapidly in 2018, in part due to the release of the CoBottoolkit.

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize