Team Sounding Board Alexa Prize.jpg

Alexa Prize SocialBot Grand Challenge 1

Congratulations to Team Sounding Board from the University of Washington.

Winner: Team Sounding Board, University of Washington, Seattle, WA, USA

With an average score of 3.17 and average duration of 10 minutes and 22 seconds, Team Sounding Board will share the $500,000 prize among the student team members.

Second: Team Alquist, Czech Technical University, Prague, Czech Republic

Team Alquist will share a $100,000 prize among the student team members.

Third: Team What's Up Bot, Heriot Watt University, Edinburgh, Scotland, UK

Team What's Up Bot will share a $50,000 prize among the student team members.

Alexa Prize SocialBot Grand Challenge 1

Proceedings

The Alexa Prize Proceedings publishes the research in Conversational AI resulting from the pursuit of the Alexa Prize competition goals.

Amazon works closely with university teams to provide a testbed for research to address the challenges with Dialog Management, Natural Language Understanding (NLU), Contextual Modeling, Commonsense Reasoning and Response Generation, and these proceedings seek to capture the advances in those areas that result from these efforts. Authors are free to make additional hardcopy publishing arrangements, but Amazon will not produce hardcopies of these volumes.

Amazon Alexa Prize
Conversational AI: The Science Behind The Alexa Prize

Carnegie Mellon University - CMU Magnus
Building CMU Magnus from User Feedback

Carnegie Mellon University - RubyStar
A Non-Task-Oriented Mixture Model Dialog System

Czech Technical University - Alquist
The Alexa Prize Socialbot

Emory University - EmersonBot
Information-Focused Conversational AI Emory University at the Alexa Prize 2017 Challenge

Heriot-Watt University - Alana
Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback

Princeton University - Pixie
A Social Chatbot

Rensselaer Polytechnic Institute - Wise Macaw
A Two-Layer Dialogue Framework For Authoring Social Bots

Seoul National University - Chatty Chat
A Chatbot by Combining Finite State Machine, Information Retrieval, and Bot-Initiative Strategy

University of California Berkeley - Eigen
A Step Towards Conversational AI

University of California Santa Cruz - SlugBot
An Application of a Novel and Scalable Open Domain Socialbot Framework

University of Edinburgh - Edina
Building an Open Domain Socialbot with Self-dialogues

University of Montreal - Mila Team
The Octopus Approach to the Alexa Competition: A Deep Ensemble-based Socialbot

University of Trento - Roving Mind
A balancing act between open–domain and engaging dialogue systems

University of Washington - Sounding Board
University of Washington’s Alexa Prize Submission

You can also download all of the papers in one .zip file.

Latest news

The latest updates, stories, and more about Alexa Prize.
US, WA, Bellevue
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US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
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US, MA, Boston
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US, MA, Boston
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