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Deep Reinforcement Learning for Goal oriented Dialogue Systems

Despite recent advancements in NLP, Dialogue Policy managers in most deployed Dialogue systems are still hand-coded and require a considerable amount of human effort.

These manually coded dialogue policies tend to be static and deteriorate over time due to a lack of adaptation to changes in the environment like new products and changing user behavior.

In this talk, Rajesh will demonstrate how Deep Reinforcement Learning can be used to automatically extract optimal dialogue policies from unannotated conversation logs.

Serve banking customers by leveraging Natural Language Processing

In this talk, Andy Li will focus on discussing how they use NLP to understand customer needs and enhance customer experiences at...