Existing data-to-text generation datasets are mostly limited to English. To
address this lack of data, we create Table-to-Text in African languages (TaTa),
the first large multilingual table-to-text dataset with a focus on African
languages. We created TaTa by transcribing figures and accompanying text in
bilingual reports by the Demographic and Health Surveys Program, followed by
professional translation to make the dataset fully parallel. TaTa includes
8,700 examples in nine languages including four African languages (Hausa, Igbo,
Swahili, and Yorùbá) and a zero-shot test language (Russian). We
additionally release screenshots of the original figures for future research on
multilingual multi-modal approaches. Through an in-depth human evaluation, we
show that TaTa is challenging for current models and that less than half the
outputs from an mT5-XXL-based model are understandable and attributable to the
source data. We further demonstrate that existing metrics perform poorly for
TaTa and introduce learned metrics that achieve a high correlation with human
judgments. We release all data and annotations at
this https URL.

该研究创建了第一个专注于非洲语言的大型多语言表格生成文本数据集：TaTa，并通过深入的人类评估表明，TaTa 对当前模型具有挑战性，现有指标的表现不佳。