Alquist from the Czech Technical University.jpg

Alexa Prize SocialBot Grand Challenge 4

Congratulations to Team Alquist from the Czech Technical University.

Team Alquist from Czech Technical University won the Alexa Prize SocialBot Grand Challenge 4 competition, and was awarded the $500,000 first prize for earning the top score in the finals competition.

Czech Technical University Alexa Prize SocialBot Grand Challenge 4.jpg

Although none of this year’s teams met the Grand Challenge, each finalist demonstrated impressive progress toward the goal. Alquist, the socialbot from CTU, earned first place with a 3.28 average rating, and an average finals’ competition interaction duration of 14 minutes and 14 seconds.

For the second consecutive year, Stanford University’s Chirpy Cardinal socialbot earned second-place honors and a $100,000 prize by achieving a 3.25 average rating, and an average of 13 minutes and 25 seconds of interaction duration. Proto, the socialbot from the University of Buffalo team, earned third-place honors with an average rating of 3.16, and an average of 14 minutes and 45 seconds of interaction duration.

Alexa Prize SocialBot Grand Challenge 4

Key dates

September 9, 2020
Application period opens

October 23, 2020
Application period closes

October 2020
Competing teams announced

Fall 2020
Teams onboarded

November 2020
Competition begins

July 2021
Finals

August 2021
Winners announced

How to apply

The Alexa Prize application is hosted on the YouNooodle platform. To begin your application you must have a YouNoodle account. Please create your account by clicking “Create Account” or login below using your existing YouNoodle.com credentials. If you have started your application a link to it will also show below.

To accommodate proposers adversely affected by the ongoing pandemic to complete their applications, we are extending the submission deadline to October 23rd. In the interest of fairness, the University teams who have already submitted proposals may also use this additional time to update their proposals if they want to. All other Alexa Prize related dates (e.g. announcement of selected proposals) will remain the same.

Proceedings

Forward
Alexa Prize Socialbot Grand Challenge Year IV

Amazon Alexa Prize
Further advances in open domain dialog systems in the Fourth Alexa Prize SocialBot Grand Challenge

Czech Technical University in Prague - Alquist
Alquist 4.0: Towards social intelligence using generative models and dialogue personalization

Emory University - Emora
An approach to inference-driven dialogue management within a social chatbot

Moscow Institute of Physics and Technology - DREAM
DREAM Technical Report for the Alexa Prize 4

Polytechnic University of Madrid - Genuine2
Genuine2: An open domain chatbot based on generative models

Stanford University - Chirpy Cardinal
Neural, neural everywhere: controlled generation meets scaffolded, structured dialogue

Suny Buffalo - Proto
Proto: A neural cocktail for generating appealing conversations

University of California, Santa Cruz - Athena
Athena 2.0: Discourse and user modeling in open domain dialogue

University of Southern California - Viola
Viola: A topic agnostic generate-and-rank dialogue system

University of Texas at Dallas - CASPR
CASPR: A commonsense reasoning-based conversational socialbot

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, Seattle
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IN, KA, Bengaluru
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IN, KA, Bengaluru
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IN, KA, Bengaluru
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US, CA, San Diego
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IN, KA, Bengaluru
The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. As an Applied Science Manager on the team, you will lead a team of scientists to define and execute a transformative vision for holistic catalog quality measurement, metadata enrichment, and content integrity. Your team will own the science solutions for foundational quality detection frameworks, metadata validation and correction technologies, state-of-the-art algorithms to identify and resolve catalog anomalies (violative content, duplicative/low value content, misattributed tracks, incorrect metadata), and/or agentic AI solutions that help internal teams quickly surface and fix quality issues to ensure customers receive accurate, complete catalog experiences. Key job responsibilities You independently manage a team of scientists. You identify the needs of your team and effectively grow, hire, and promote scientists to maintain a high-performing team. You have a broad understanding of scientific techniques, several of which may fall out of your specific job function. You define the strategic vision for your team. You establish a roadmap and successfully deliver scientific solutions that innovate on catalog quality detection, metadata enrichment, and content integrity. You define clear goals for your team and effectively prioritize, balancing short-term quality improvements and long-term innovation in catalog intelligence. You establish clear and effective metrics and scientific process to enforce consistent, high-quality artifact delivery and measurable catalog quality improvements. You proactively identify risks and bring them to the attention of your manager, customers, and stakeholders with plans for mitigation before they become roadblocks. You know when to escalate. You communicate ideas effectively, both verbally and in writing, to all types of audiences. You author strategic documentation for your team. You communicate issues and options with leaders in such a way that facilitates understanding and that leads to a decision. You work successfully with customers, leaders, and engineering teams. You foster a constructive dialogue, harmonize discordant views, and lead the resolution of contentious issues. About the team We are a team of scientists and MLEs focused on music catalog quality and metadata intelligence. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and data quality systems with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, content operations teams, and other scientists with expertise in music metadata, content classification, and building scalable modeling and software solutions that keep the Amazon Music catalog accurate, complete, and trustworthy.
US, CA, San Diego
Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.
US, WA, Seattle
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BR, SP, Sao Paulo
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US, CA, Sunnyvale
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