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        <title>End-to-End Task-Completion Neural Dialogue Systems</title>
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        <namePart type="given">Xiujun</namePart>
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    <name type="personal">
        <namePart type="given">Lihong</namePart>
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    <name type="personal">
        <namePart type="given">Jianfeng</namePart>
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    <abstract>One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.</abstract>
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%0 Conference Proceedings
%T End-to-End Task-Completion Neural Dialogue Systems
%A Li, Xiujun
%A Chen, Yun-Nung
%A Li, Lihong
%A Gao, Jianfeng
%A Celikyilmaz, Asli
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F li-etal-2017-end
%X One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.
%U https://aclanthology.org/I17-1074
%P 733-743

Markdown (Informal)

[End-to-End Task-Completion Neural Dialogue Systems](https://aclanthology.org/I17-1074) (Li et al., IJCNLP 2017)

• End-to-End Task-Completion Neural Dialogue Systems (Li et al., IJCNLP 2017)

ACL

• Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao, and Asli Celikyilmaz. 2017. End-to-End Task-Completion Neural Dialogue Systems. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 733–743, Taipei, Taiwan. Asian Federation of Natural Language Processing.