@inproceedings{wang-etal-2017-group,
title = "Group Linguistic Bias Aware Neural Response Generation",
author = "Wang, Jianan and
Wang, Xin and
Li, Fang and
Xu, Zhen and
Wang, Zhuoran and
Wang, Baoxun",
editor = "Zhang, Yue and
Sui, Zhifang",
booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
month = dec,
year = "2017",
address = "Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-6001",
pages = "1--10",
abstract = "For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.",
}
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<abstract>For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.</abstract>
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%0 Conference Proceedings
%T Group Linguistic Bias Aware Neural Response Generation
%A Wang, Jianan
%A Wang, Xin
%A Li, Fang
%A Xu, Zhen
%A Wang, Zhuoran
%A Wang, Baoxun
%Y Zhang, Yue
%Y Sui, Zhifang
%S Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing
%D 2017
%8 December
%I Association for Computational Linguistics
%C Taiwan
%F wang-etal-2017-group
%X For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.
%U https://aclanthology.org/W17-6001
%P 1-10
Markdown (Informal)
[Group Linguistic Bias Aware Neural Response Generation](https://aclanthology.org/W17-6001) (Wang et al., SIGHAN 2017)
ACL
- Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, and Baoxun Wang. 2017. Group Linguistic Bias Aware Neural Response Generation. In Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing, pages 1–10, Taiwan. Association for Computational Linguistics.