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

Nov 1, 2021·
Clement Rebuffel
,
Thomas Scialom
,
Laure Soulier
,
Benjamin Piwowarski
,
Sylvain Lamprier
,
Jacopo Staiano
,
Geoffrey Scoutheeten
,
Patrick Gallinari
· 0 min read
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.
Type
Publication
EMNLP 2021