  In this paper, we explore how QuestEval, which is a Text-vs-Text metric, can
be adapted for the evaluation of Data-to-Text Generation systems. QuestEval is
a reference-less metric that compares the predictions directly to the
structured input data by automatically asking and answering questions. Its
adaptation to Data-to-Text is not straightforward as it requires multi-modal
Question Generation and Answering (QG \& QA) systems. To this purpose, we
propose to build synthetic multi-modal corpora that enables to train
multi-modal QG/QA. The resulting metric is reference-less, multi-modal; it
obtains state-of-the-art correlations with human judgement on the E2E and
WebNLG benchmark.
