Implications of Deep Learning for Dialog Modeling

A special session at Sigdial 2019

Although deep learning has transformed many speech technologies, the impact on dialog has so far been more modest. This special session will explore opportunities and challenges for dialog research in the deep learning era.

Full Papers

TBA

Late Breaking Results

Domain-Independent Turn-Level Dialogue Quality Estimation via User Satisfaction Estimation. Praveen Kumar Bodigutla, Longshaokan Wang, Kate Ridgeway, Joshua Levy, Swanand Joshi, Alborz Geramifard, Spyros Matsoukas.

Predictive Turn-Taking Decisions with POMDPs. Matthew Roddy, Naomi Harte

Motivating Questions (from the Call for Papers)

Submission Process

Full papers were submitted through the general SIGdial mechanism, were selected after evaluation by the regular SIGdial peer review process and were presented orally. Work-in-progress (late-breaking results) papers were solicited as presentations of ongoing work, circumscribed contributions, or focused programmatic proposals. These were reviewed by the special session organizing committee and presented as posters.

Organizers

Yun-Nung (Vivian) Chen, National Taiwan University

Gabriel Skantze, KTH, Stockholm

Tatsuya Kawahara, Kyoto University

Nigel G. Ward, University of Texas at El Paso