Robust Conversational AI with Grounded Text Generation

Abstract:

In this talk, I present a hybrid approach based on a Grounded Text Generation (GTG) model to building robust task bots at scale.  GTG is a hybrid model which uses a large-scale Transform neural network as its backbone, combined with symbol manipulation modules for knowledge base inference and prior knowledge encoding, to generate responses grounded in dialog belief state and real-world knowledge for task completion.  GTG is pre-trained on large amounts of raw text and human conversational data, and can be fine-tuned to complete a wide range of tasks. 

The hybrid approach and its variants are being developed simultaneously by multiple research teams. The primary results reported on task-oriented dialog benchmarks are very promising, demonstrating big potential of this approach. I provide an overview of this progress and discuss related methods and technologies that can be incorporated for building robust conversational AI systems.

Bio:   Jianfeng Gao is a Distinguished Scientist and Vice President of Microsoft. He is the manager of the Deep Learning (DL) group at Microsoft Research, leading the development of AI systems for natural language processing, Web search, vision language understanding, dialogue, and business applications. He is an IEEE fellow.

For Zoom URL contact kate.cowperthwaite@queensu.ca