While some recent papers on knowledge-grounded dialogues have paid attention to the problem of knowledge selection (Lian et al., 2019; Kim et al., 2020; Ren et al., 2019), the knowledge selection module is either deeply coupled with the specially configured models (Lian et al., 2019; Ren et al., 2019) and thus is incompatible with the pre-trained language models, or it is learned with human annotations (Dinan et al., 2019; Kim et al., 2018) which are difficult to obtain in practice (e.g., the dataset in (Zhou et al., 2018b) does not contain annotations for knowledge selection).
we propose an unsupervised approach where learning of knowledge selection and fine-tuning of response generation are jointly conducted with unlabeled dialogues.