  Conversational agents are exploding in popularity. However, much work remains
in the area of social conversation as well as free-form conversation over a
broad range of domains and topics. To advance the state of the art in
conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar
university competition where sixteen selected university teams were challenged
to build conversational agents, known as socialbots, to converse coherently and
engagingly with humans on popular topics such as Sports, Politics,
Entertainment, Fashion and Technology for 20 minutes. The Alexa Prize offers
the academic community a unique opportunity to perform research with a live
system used by millions of users. The competition provided university teams
with real user conversational data at scale, along with the user-provided
ratings and feedback augmented with annotations by the Alexa team. This enabled
teams to effectively iterate and make improvements throughout the competition
while being evaluated in real-time through live user interactions. To build
their socialbots, university teams combined state-of-the-art techniques with
novel strategies in the areas of Natural Language Understanding, Context
Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To
support the efforts of participating teams, the Alexa Prize team made
significant scientific and engineering investments to build and improve
Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice
User Experience, and tools for traffic management and scalability. This paper
outlines the advances created by the university teams as well as the Alexa
Prize team to achieve the common goal of solving the problem of Conversational
AI.
