@inproceedings{shu-etal-2019-flexibly,
title = "Flexibly-Structured Model for Task-Oriented Dialogues",
author = "Shu, Lei and
Molino, Piero and
Namazifar, Mahdi and
Xu, Hu and
Liu, Bing and
Zheng, Huaixiu and
Tur, Gokhan",
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://aclanthology.org/W19-5922",
doi = "10.18653/v1/W19-5922",
pages = "178--187",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Flexibly-Structured Model for Task-Oriented Dialogues
%A Shu, Lei
%A Molino, Piero
%A Namazifar, Mahdi
%A Xu, Hu
%A Liu, Bing
%A Zheng, Huaixiu
%A Tur, Gokhan
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F shu-etal-2019-flexibly
%X 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.
%R 10.18653/v1/W19-5922
%U https://aclanthology.org/W19-5922
%U https://doi.org/10.18653/v1/W19-5922
%P 178-187
Markdown (Informal)
[Flexibly-Structured Model for Task-Oriented Dialogues](https://aclanthology.org/W19-5922) (Shu et al., SIGDIAL 2019)
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.