Date: Friday, August 30, 2019 - Room 1
|8:45-9:00||Welcome and Introduction|
|9:00-10:00||Keynote by Nikola Mrksic
Deploying a Conversational AI platform for Customer Support
Abstract: PolyAI is a London-based startup with a leading machine learning platform for conversational agents. The deployed AI agents understand users, hold conversations without getting confused and can easily scale to new use cases or other languages. This talk will present the machine learning techniques that underpin the PolyAI platform and results from its early deployments in contact centre environments.
|10:00-10:30||Answering Complex Queries with Heterogeneous Structured Knowledge Sources extracted from Text
Nikita Bhutani (University of Michigan, Ann Arbor, Michigan)*
|10:30-11:00||Coffee Break (30 min)|
Leveraging Human Learning in Interactive Data Exploration
DBPal: Weak Supervision for Learning a Natural Language Interface to Databases
Disambiguating Natural Language Queries with Tuples
|12:30-13:30||Lunch Break (60 min)|
|14:00-15:00||Keynote by Dilek Hakkani-Tur
Conversational Machines: Bridging the chasm between task-oriented and social conversations
Abstract: Conversational systems generally fall into two categories: task-oriented and social bots. Task-oriented systems aim to help users accomplish a specific task through multi-turn interactions, whereas socialbots focus on engaging and natural open-domain conversations. In natural interactions, even when conversation participants have a task or goal in mind, they can say things that are out of the boundaries of that task domain, and similarly they can switch to a task during a chitchat conversation. Hence, the ability to engage in knowledgeable social interactions and gracefully transition back and forth to the task is crucial for enabling natural conversations.
In this talk, I’ll summarize our recent work in both fronts, focusing on the convergence of approaches for the two categories of conversational systems. Starting with task-oriented interactions, I’ll present our approach for bootstrapping task-oriented dialogue systems from simulated seeker-provider conversations and dialogue state tracking using generate-and-copy mechanisms. This will be followed by a summary of learnings from previous Alexa Prize challenges and progression of our work as we approach the next challenge.
|15:00-15:30||Building a Hotel Concierge Bot: an industrial case study
Behzad Golshan (Megagon Labs)*; George Mihaila (Megagon Labs); Chen Chen (Megagon Labs); Jonathan Engel (Megagon Labs); Alon Halevy (Megagon Labs); Yoshihiko Suhara (Megagon Labs); Wang-Chiew Tan (Megagon Labs); Michael Matuschek (TrustYou)
|15:30-16:00||Coffee Break (30 min)|
|16:00-17:30||Industry panel on "Conversational Access to Data and Enterprise Chatbots"|