@inproceedings{zhang-etal-2020-multi-turn,
title = "Multi-Turn Dialogue Generation in {E}-Commerce Platform with the Context of Historical Dialogue",
author = "Zhang, WeiSheng and
Song, Kaisong and
Kang, Yangyang and
Wang, Zhongqing and
Sun, Changlong and
Liu, Xiaozhong and
Li, Shoushan and
Zhang, Min and
Si, Luo",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.179",
doi = "10.18653/v1/2020.findings-emnlp.179",
pages = "1981--1990",
abstract = "As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers{'} historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers{'} historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller{'}s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers{'} characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.",
}
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<abstract>As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.</abstract>
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%0 Conference Proceedings
%T Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
%A Zhang, WeiSheng
%A Song, Kaisong
%A Kang, Yangyang
%A Wang, Zhongqing
%A Sun, Changlong
%A Liu, Xiaozhong
%A Li, Shoushan
%A Zhang, Min
%A Si, Luo
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-multi-turn
%X As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.
%R 10.18653/v1/2020.findings-emnlp.179
%U https://aclanthology.org/2020.findings-emnlp.179
%U https://doi.org/10.18653/v1/2020.findings-emnlp.179
%P 1981-1990
Markdown (Informal)
[Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue](https://aclanthology.org/2020.findings-emnlp.179) (Zhang et al., Findings 2020)
ACL
- WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, and Luo Si. 2020. Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1981–1990, Online. Association for Computational Linguistics.