Do It Once: An Embarrassingly Simple Joint Matching Approach to Response Selection

Abstract

Existing matching models for response selection adopt the independent matching (IM) approach. To complete a prediction, they have to perform N independent matches, where N is the number of response options. In this paper, we explore a joint matching (JM) approach which performs matching only once regardless of the number of options. The JM approach does not change the structure of matching component but only modifies its input and output format. It also enables a cheap but effective data augmentation method. Extensive experiments on the MuTual dataset demonstrate that, even with the simplest formulation, JM outperforms IM approach by a large margin and reduces training time by over half.