  We explore question generation in the context of knowledge-grounded dialogs
focusing on explainability and evaluation. Inspired by previous work on
planning-based summarisation, we present a model which instead of directly
generating a question, sequentially predicts first a fact then a question. We
evaluate our approach on 37k test dialogs adapted from the KGConv dataset and
we show that, although more demanding in terms of inference, our approach
performs on par with a standard model which solely generates a question while
allowing for a detailed referenceless evaluation of the model behaviour in
terms of relevance, factuality and pronominalisation.
