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        <title>Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation</title>
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        <namePart type="given">Yi-An</namePart>
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        <namePart type="given">Arshit</namePart>
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    <name type="personal">
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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            <namePart type="family">Villavicencio</namePart>
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    <abstract>Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1075</identifier>
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        <url>https://aclanthology.org/K19-1075</url>
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%0 Conference Proceedings
%T Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
%A Lai, Yi-An
%A Gupta, Arshit
%A Zhang, Yi
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lai-etal-2019-goal
%X Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.
%R 10.18653/v1/K19-1075
%U https://aclanthology.org/K19-1075
%U https://doi.org/10.18653/v1/K19-1075
%P 798-811

Markdown (Informal)

[Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation](https://aclanthology.org/K19-1075) (Lai et al., CoNLL 2019)

• Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation (Lai et al., CoNLL 2019)

ACL

• Yi-An Lai, Arshit Gupta, and Yi Zhang. 2019. Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 798–811, Hong Kong, China. Association for Computational Linguistics.