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Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
Building open domain conversational systems that allow users to have eng...
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On Evaluating and Comparing Conversational Agents
Conversational agents are exploding in popularity. However, much work re...
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Evaluating Visual Conversational Agents via Cooperative Human-AI Games
As AI continues to advance, human-AI teams are inevitable. However, prog...
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A Semantic Cross-Species Derived Data Management Application
Managing dynamic information in large multi-site, multi-species, and mul...
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Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System
This is the application document for the 2019 Amazon Alexa competition. ...
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Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents
This work compares user collaboration with conversational personal assis...
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A Talker Ensemble: the University of Wrocław's Entry to the NIPS 2017 Conversational Intelligence Challenge
We present Poetwannabe, a chatbot submitted by the University of Wrocław...
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Conversational AI: The Science Behind the Alexa Prize
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.
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