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arXiv · 2104.07555

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

Clément Rebuffel·Thomas Scialom·Laure Soulier·Benjamin Piwowarski·Sylvain Lamprier·Jacopo Staiano·Geoffrey Scoutheeten·Patrick Gallinari
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Abstract

QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward, as it requires multimodal Question Generation and Answering systems on the considered tasks, which are seldom available. To this purpose, we propose a method to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. The resulting metric is reference-less and multimodal; it obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. We make data-QuestEval's code and models available for reproducibility purpose, as part of the QuestEval project.

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Clément Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari. 2021-04-15. Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation. https://arxiv.org/abs/2104.07555

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