@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://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/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."
}