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Advancing conversational task assistance: the second Alexa Prize TaskBot challenge

By Eugene Agichtein, Michael Johnston, Anna Gottardi, Lavina Vaz, Cris Flagg, Yao Lu, Shaohua Liu, Sattvik Sahai, Giuseppe Castellucci, Jason Ingyu Choi, Prasoon Goyal, Di Jin, Saar Kuzi, Nikhita Vedula, Lucy Hu, Samyuth Sagi, Luke Dai, Hangjie Shi, Zhejia Yang, Desheng Zhang, Chao Zhang, Daniel Pressel, Heather Rocker, Leslie Ball, Osman Ipek, Kate Bland, James Jeun, Oleg Rokhlenko, Akshaya Iyengar, Arindam Mandal, Yoelle Maarek, Reza Ghanadan
2023
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Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students and faculty to explore and compete in the development of conversational agents through the SocialBot Grand Challenge, whose goal is to build agents capable of conversing coherently and engagingly with humans on popular topics. As conversational agents attempt to assist users with increasingly complex tasks, new conversational AI techniques and evaluation platforms are needed. The Alexa Prize TaskBot Challenge, now in its second year, introduced the requirements of interactively assisting humans with real-world tasks, while making use of both voice and visual modalities. This challenge requires the TaskBots to identify and understand the user’s need, identify and integrate task and domain knowledge into the interaction, and develop new ways of engaging the user without distracting them from the task at hand, among other challenges. This paper provides an overview of the second TaskBot challenge, in which both new and returning teams participated. We describe the infrastructure support and the new models provided to the teams with the CoBot Toolkit. We then summarize the approaches the participating teams took to address research challenges, including changes and improvements from the previous year. Finally, we analyze the performance of the competing TaskBots and discuss some of the lessons learned.

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