@inproceedings{zhu-etal-2018-lingke,
title = "{L}ingke: a Fine-grained Multi-turn Chatbot for Customer Service",
author = "Zhu, Pengfei and
Zhang, Zhuosheng and
Li, Jiangtong and
Huang, Yafang and
Zhao, Hai",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2024",
pages = "108--112",
abstract = "Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.",
}
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%0 Conference Proceedings
%T Lingke: a Fine-grained Multi-turn Chatbot for Customer Service
%A Zhu, Pengfei
%A Zhang, Zhuosheng
%A Li, Jiangtong
%A Huang, Yafang
%A Zhao, Hai
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F zhu-etal-2018-lingke
%X Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.
%U https://aclanthology.org/C18-2024
%P 108-112
Markdown (Informal)
[Lingke: a Fine-grained Multi-turn Chatbot for Customer Service](https://aclanthology.org/C18-2024) (Zhu et al., COLING 2018)
ACL
- Pengfei Zhu, Zhuosheng Zhang, Jiangtong Li, Yafang Huang, and Hai Zhao. 2018. Lingke: a Fine-grained Multi-turn Chatbot for Customer Service. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 108–112, Santa Fe, New Mexico. Association for Computational Linguistics.