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QuestEval is a reference-less metric used in text-to-text tasks, thatcompares the generated summaries directly to the source text, by automaticallyasking and answering questions. Its adaptation to Data-to-Text tasks is notstraightforward, as it requires multimodal Question Generation and Answeringsystems on the considered tasks, which are seldom available. To this purpose,we propose a method to build synthetic multimodal corpora enabling to trainmultimodal components for a data-QuestEval metric. The resulting metric isreference-less and multimodal; it obtains state-of-the-art correlations withhuman judgment on the WebNLG and WikiBio benchmarks. We make data-QuestEval'scode and models available for reproducibility purpose, as part of the QuestEvalproject.

Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation