Abstract: Human conversation attempts to build common ground consisting of shared beliefs, knowledge, and perceptions that form the premise for understanding utterances. Recent deep learning-based dialogue systems use human dialogue data to train a mapping from a dialogue history to responses, but common ground not directly expressed in words makes it difficult to generate coherent responses by learning statistical patterns alone. We propose Dialogue Completion using Zero Anaphora Resolution (DCZAR), a framework that explicitly completes omitted information in the dialogue history and generates responses from the completed dialogue history. In this study, we conducted automatic and human evaluations by applying several pretraining methods and datasets in Japanese in various combinations. Experimental results show that the DCZAR framework contributes to the generation of more coherent and engaging responses.