SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System

Understand

The recent advent of neural approaches for developing each dialog component in task-oriented dialog systems has remarkably improved, yet optimizing the overall system performance remains a challenge.

• Besides, previous research on modeling complicated multi-domain goal-oriented dialogs in end-to-end fashion has been limited.
• In this paper, we present an effective multi-domain end-to-end trainable neural dialog system SUMBT+LaRL that incorporates two previous strong models and facilitates them to be fully differentiable.
• Specifically, the SUMBT+ estimates user-acts as well as dialog belief states, and the LaRL models latent system action spaces and generates responses given the estimated contexts.