  In this work, we present a model for document-grounded response generation in
dialog that is decomposed into two components according to Bayes theorem. One
component is a traditional ungrounded response generation model and the other
component models the reconstruction of the grounding document based on the
dialog context and generated response. We propose different approximate
decoding schemes and evaluate our approach on multiple open-domain and
task-oriented document-grounded dialog datasets. Our experiments show that the
model is more factual in terms of automatic factuality metrics than the
baseline model. Furthermore, we outline how introducing scaling factors between
the components allows for controlling the tradeoff between factuality and
fluency in the model output. Finally, we compare our approach to a recently
proposed method to control factuality in grounded dialog, CTRL
(arXiv:2107.06963), and show that both approaches can be combined to achieve
additional improvements.
