Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
This addresses the challenge of making chatbots more personalized and engaging for users, representing an incremental improvement over existing methods.
The paper tackles the problem of incorporating personality into retrieval-based chatbots by proposing a dually interactive matching network (DIM), which outperforms its baseline by 14.5% and the state-of-the-art by 27.7% in top-1 accuracy on the PERSONA-CHAT dataset.
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.