  As large dialogue models become commonplace in practice, the problems
surrounding high compute requirements for training, inference and larger memory
footprint still persists. In this work, we present AUTODIAL, a multi-task
dialogue model that addresses the challenges of deploying dialogue model.
AUTODIAL utilizes parallel decoders to perform tasks such as dialogue act
prediction, domain prediction, intent prediction, and dialogue state tracking.
Using classification decoders over generative decoders allows AUTODIAL to
significantly reduce memory footprint and achieve faster inference times
compared to existing generative approach namely SimpleTOD. We demonstrate that
AUTODIAL provides 3-6x speedups during inference while having 11x fewer
parameters on three dialogue tasks compared to SimpleTOD. Our results show that
extending current dialogue models to have parallel decoders can be a viable
alternative for deploying them in resource-constrained environments.
