Most chatbots and voice skills are based on a state machine and too many if/else statements. Tom will show you how to move past that and build flexible, robust experiences using machine learning throughout the stack.
Conversational software is everywhere: messaging apps have opened up APIs to bot developers and millions of consumers now own voice controlled speakers. But the tools and frameworks for building these systems are still immature. Tom will talk about Rasa, an open source machine learning framework for building conversational software. The talk will cover the algorithms Rasa uses to build flexible and robust voice and text systems, the trade offs in using supervised versus reinforcement learning, and whether it's really such a good idea to generate text with LSTMs. Outline:
Components : NLU , DM , integration , NLG Overview of available tools and frameworks Describe how Rasa does NLU Motivation & a chatbot leading to state machine hell How Rasa does dialogue management. How to advance a bots capabilities - closing the loop and data collection. Current research topics and challenges.