@inproceedings{gu-etal-2019-dually,
title = "Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots",
author = "Gu, Jia-Chen and
Ling, Zhen-Hua and
Zhu, Xiaodan and
Liu, Quan",
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/D19-1193",
doi = "10.18653/v1/D19-1193",
pages = "1845--1854",
abstract = "This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approach which enhances the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5{\%} and outperforms the present state-of-the-art model by a margin of 27.7{\%} in terms of top-1 accuracy hits@1.",
}
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<abstract>This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approach which enhances the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5% and outperforms the present state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.</abstract>
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%0 Conference Proceedings
%T Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
%A Gu, Jia-Chen
%A Ling, Zhen-Hua
%A Zhu, Xiaodan
%A Liu, Quan
%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 gu-etal-2019-dually
%X This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approach which enhances the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5% and outperforms the present state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.
%R 10.18653/v1/D19-1193
%U https://aclanthology.org/D19-1193
%U https://doi.org/10.18653/v1/D19-1193
%P 1845-1854
Markdown (Informal)
[Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots](https://aclanthology.org/D19-1193) (Gu et al., EMNLP-IJCNLP 2019)
ACL