Flexibly-Structured Model for Task-Oriented Dialogues
Lei Shu, Piero Molino, Mahdi Namazifar, Hu Xu, Bing Liu, Huaixiu Zheng, Gokhan Tur
Abstract
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot. The policy engine and language generation tasks are modeled jointly following that. The copy-augmented sequential decoder deals with new or unknown values in the conversation, while the multi-label decoder combined with the sequential decoder ensures the explicit assignment of values to slots. On the generation part, slot binary classifiers are used to improve performance. This architecture is scalable to real-world scenarios and is shown through an empirical evaluation to achieve state-of-the-art performance on both the Cambridge Restaurant dataset and the Stanford in-car assistant dataset.- Anthology ID:
- W19-5922
- 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:
- 178–187
- Language:
- URL:
- https://aclanthology.org/W19-5922
- DOI:
- 10.18653/v1/W19-5922
- Cite (ACL):
- Lei Shu, Piero Molino, Mahdi Namazifar, Hu Xu, Bing Liu, Huaixiu Zheng, and Gokhan Tur. 2019. Flexibly-Structured Model for Task-Oriented Dialogues. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 178–187, Stockholm, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Flexibly-Structured Model for Task-Oriented Dialogues (Shu et al., SIGDIAL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-5922.pdf
- Code
- uber-research/FSDM