Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

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-CHA T 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. 1 Introduction Building a conversation system with intelligence is challenging. Response selection, which aims to select a potential response from a set of candidates given the context of a conversation, is an important technique to build retrieval-based chatbots (Zhou et al., 2018). Many previous studies on single-turn (Wang et al., 2013) or multi-turn response selection (Lowe et al., 2015; Zhou et al., 2018; Gu et al., 2019) rank response candidates according to their semantic relevance with the given context. With the emergence and popular use of personal assistants such as Apple Siri, Google Now and Microsoft Cortana, the techniques of making personalized dialogues has attracted much research attention in recent years (Li et al., 2016; Zhang et al., 2018; Mazar e et al., 2018). Zhang et al. (2018) constructed a PERSONA-CHA T dataset for building personalized dialogue agents, where each persona was represented as multiple sentences of profile description. An example dialogue conditioned on given profiles from this dataset is given in Table 1 for illustration.