TaTA: A Multilingual Table-to-Text Dataset for African Languages

Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera

Abstract
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. Our results highlight a) the need for validating metrics; and b) the importance of domain-specific metrics.
Anthology ID:
2023.findings-emnlp.118
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1719–1740
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.118
DOI:
10.18653/v1/2023.findings-emnlp.118
Bibkey:
Cite (ACL):
Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, and Clara Rivera. 2023. TaTA: A Multilingual Table-to-Text Dataset for African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1719–1740, Singapore. Association for Computational Linguistics.
Cite (Informal):
TaTA: A Multilingual Table-to-Text Dataset for African Languages (Gehrmann et al., Findings 2023)
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