End-to-End Conversational System for Customer Service Application
Goal-oriented conversational systems are typically multi-turn, relying on the entire conversation thus far to generate a response to user input. Many of these systems use dialog state tracking or belief tracking, to either rank candidate responses from a pool of templates or generate responses directly while others are end-to-end. End-to-end models for goal-oriented conversational systems have become an increasingly active area of research.
In this talk, I will present our recent efforts to build end-to-end conversational models for customer service application. We use historical chat transcripts and customer profile data to build models, and test with live customers using a human-in-the-loop research platform. We experiment with sequence-to-sequence model that generates responses word by word, and multi-encoder based ranking model to score template responses. I will compare these approaches as they apply to customer service domain.
Manisha Srivastava is a machine learning scientist at Amazon Customer Service, working to improve the customer experience using NLP techniques. Prior to Amazon she worked at TripAdvisor, focusing on using machine learning to improve the quality of business listings. She received her masters degree in Computer Engineering from Texas A&M University, College Station. Before this she worked as data scientist at General Electric Research lab India, applying predictive analytics in domains like healthcare, oil & gas, and wind energy. She did her undergraduate studies at Indian Institute of Technology, Guwahati.