Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies
Sarah M. McLeod, Ivana Kruijff‐Korbayová, Bernd Kiefer · 2019
This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection.These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets.We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain.Results show the proposed models improve over models trained without knowledge of NLU tasks.