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 approaches which enhance 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 current state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.
@article{arxiv.1908.05859,
title = {Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots},
author = {Jia-Chen Gu and Zhen-Hua Ling and Xiaodan Zhu and Quan Liu},
journal= {arXiv preprint arXiv:1908.05859},
year = {2020}
}