Qrange-group/CEM
EMNLP'22, CEM improves MHCH performance by correcting prediction bias and training an auxiliary cost simulator based on user state and labor cost causal graph, without requiring complex model crafting.
This project helps customer service teams decide when an automated chat system should transfer a customer to a human agent. It takes in customer dialogue data and provides an improved system for deciding the optimal moment for human intervention. Customer service managers and operations leads responsible for chat support would use this.
No commits in the last 6 months.
Use this if you manage a customer service chat operation and want to optimize when automated systems hand off conversations to human agents, balancing efficiency and customer satisfaction.
Not ideal if you are looking for a completely new chat bot or a system to generate human-like responses.
Stars
11
Forks
—
Language
Python
License
MIT
Category
Last pushed
Oct 09, 2022
Commits (30d)
0
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