@inproceedings{cai-etal-2019-retrieval,
title = "Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework",
author = "Cai, Deng and
Wang, Yan and
Bi, Wei and
Tu, Zhaopeng and
Liu, Xiaojiang and
Shi, Shuming",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1195",
doi = "10.18653/v1/D19-1195",
pages = "1866--1875",
abstract = "End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.",
}
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<abstract>End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the safe response problem. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.</abstract>
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%0 Conference Proceedings
%T Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
%A Cai, Deng
%A Wang, Yan
%A Bi, Wei
%A Tu, Zhaopeng
%A Liu, Xiaojiang
%A Shi, Shuming
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F cai-etal-2019-retrieval
%X End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the safe response problem. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.
%R 10.18653/v1/D19-1195
%U https://aclanthology.org/D19-1195
%U https://doi.org/10.18653/v1/D19-1195
%P 1866-1875
Markdown (Informal)
[Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework](https://aclanthology.org/D19-1195) (Cai et al., EMNLP-IJCNLP 2019)
ACL
- Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, and Shuming Shi. 2019. Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1866–1875, Hong Kong, China. Association for Computational Linguistics.