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        <namePart type="given">Tiancheng</namePart>
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    <abstract>Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.</abstract>
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%0 Conference Proceedings
%T Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
%A Zhao, Tiancheng
%A Lu, Allen
%A Lee, Kyusong
%A Eskenazi, Maxine
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F zhao-etal-2017-generative
%X Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.
%R 10.18653/v1/W17-5505
%U https://aclanthology.org/W17-5505
%U https://doi.org/10.18653/v1/W17-5505
%P 27-36

Markdown (Informal)

[Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability](https://aclanthology.org/W17-5505) (Zhao et al., SIGDIAL 2017)

• Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability (Zhao et al., SIGDIAL 2017)

ACL

• Tiancheng Zhao, Allen Lu, Kyusong Lee, and Maxine Eskenazi. 2017. Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 27–36, Saarbrücken, Germany. Association for Computational Linguistics.