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.- Anthology ID:
- W19-5947
- Volume:
- Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- September
- Year:
- 2019
- Address:
- Stockholm, Sweden
- Editors:
- Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 411–417
- Language:
- URL:
- https://aclanthology.org/W19-5947
- DOI:
- 10.18653/v1/W19-5947
- Cite (ACL):
- Sarah McLeod, Ivana Kruijff-Korbayova, and Bernd Kiefer. 2019. Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 411–417, Stockholm, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies (McLeod et al., SIGDIAL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W19-5947.pdf