@inproceedings{zhou-etal-2018-multi,
title = "Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network",
author = "Zhou, Xiangyang and
Li, Lu and
Dong, Daxiang and
Liu, Yi and
Chen, Ying and
Zhao, Wayne Xin and
Yu, Dianhai and
Wu, Hua",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1103",
doi = "10.18653/v1/P18-1103",
pages = "1118--1127",
abstract = "Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.",
}
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<abstract>Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
%A Zhou, Xiangyang
%A Li, Lu
%A Dong, Daxiang
%A Liu, Yi
%A Chen, Ying
%A Zhao, Wayne Xin
%A Yu, Dianhai
%A Wu, Hua
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhou-etal-2018-multi
%X Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.
%R 10.18653/v1/P18-1103
%U https://aclanthology.org/P18-1103
%U https://doi.org/10.18653/v1/P18-1103
%P 1118-1127
Markdown (Informal)
[Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](https://aclanthology.org/P18-1103) (Zhou et al., ACL 2018)
ACL
- Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, and Hua Wu. 2018. Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1118–1127, Melbourne, Australia. Association for Computational Linguistics.