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Amazon senior applied scientists Seokhwan Kim, left, and Alexandros Papangelis, right
Amazon senior applied scientists Seokhwan Kim, left, and Alexandros Papangelis, right, have been elected to the SIGDIAL board of members. As board members, Kim and Papangelis will further SIGDIAL’s objectives on a variety of fronts.
Glynis Condon
Conversational AI

Seokhwan Kim and Alexandros Papangelis elected to the board of members for SIGDIAL

The organization focuses on furthering the state of the art on discourse- and dialogue-related technologies.

By Staff writer
June 24, 2021
2 min read
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Amazon senior applied scientists Seokhwan Kim and Alexandros Papangelis have been elected to the SIGDIAL board of members. SIGDIAL, or the Special Interest Group on Discourse and Dialogue, is focused on furthering the state of the art on discourse- and dialogue-related technologies.

The organization, which conducts an annual conference for researchers from both industry and academia, is affiliated with the Association for Computational Linguistics, the International Speech Communication Association, and the Association for the Advancement of Artificial Intelligence. As board members, Kim and Papangelis will further SIGDIAL’s objectives on a variety of fronts, from enabling the sharing of resources, data and reusable discourse processing components, to enabling collaboration among researchers and academia.

Papangelis joined Amazon in 2019. Since then, his work has focused on developing data-conversational AI models using methods such as meta learning or sequential transfer learning. Prior to Amazon, Papangellis worked in the research departments for Uber and Toshiba. As a post-doctoral fellow at Carnegie Mellon University, Papangelis’s research focused on building socially skilled virtual agents. Papangelis earned a PhD degree in adaptive dialogue systems from the University of Texas at Arlington and the National Centre for Scientific Research “Demokritos”, a master’s degree in machine learning from University College London, and a bachelor’s degree in informatics and telecommunications from the University of Athens.

“As a researcher passionate about spoken social dialogue and dialogue management, SIGDIAL is close to my heart. The open and welcoming nature of the people involved in SIGDIAL has helped me grow as a researcher ever since I was a student,” said Papangelis. “I am delighted to join the SIGDIAL executive committee as a secretary, and help develop mechanisms to raise the bar for communications and scientific excellence within the community.”

Papangelis will be joined on SIGDIAL’s board of members by Kim. At Amazon, Kim’s research has helped develop knowledge-grounded conversational models for both open domain and task-oriented dialogue services. Before Amazon, Kim worked as a research scientist at Adobe Research and the Institute for Infocomm Research. He earned his bachelor’s degree and PhD in computer science and engineering from Pohang University of Science and Technology.

Earlier this year, Kim was also elected to the Speech and Language Processing Technical Committee for the IEEE Signal Processing Society. The Signal Processing Society was founded in 1948 as the first society for the Institute of Electronics and Electrical Engineers. Over the last seven decades, the IEEE Signal Processing Society has evolved into an association for signal processing engineers and industry professionals. Kim will join the workshops subcommittee. In this role, he will help promote IEEE-sponsored workshops, and provide new workshop organizers with resources to get them started in organizing their events.

“I am honored to be elected to the SIGDIAL board and the Speech and Language Processing Technical Committee for the IEEE Signal Processing Society,” said Kim. “I have always been passionate about leveraging the power of science to make a difference in the day-to-day lives of people, and I am excited to be able to collaborate with researchers to make this happen — both at Amazon and with researchers belonging to these institutions at large.”

Generative conversational networks
Inspired by recent work in meta-learning and generative teaching networks, the authors, which include Papangelis and Kim, propose a framework called Generative Conversational Networks, in which conversational agents learn to generate their own labelled training data (given some seed data), and then train themselves from that data to perform a given task.
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