@inproceedings{mcleod-etal-2019-multi,
    title = "Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies",
    author = "McLeod, Sarah  and
      Kruijff-Korbayova, Ivana  and
      Kiefer, Bernd",
    editor = "Nakamura, Satoshi  and
      Gasic, Milica  and
      Zukerman, Ingrid  and
      Skantze, Gabriel  and
      Nakano, Mikio  and
      Papangelis, Alexandros  and
      Ultes, Stefan  and
      Yoshino, Koichiro",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W19-5947/",
    doi = "10.18653/v1/W19-5947",
    pages = "411--417",
    abstract = "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."
}