ToTTo: A Controlled Table-To-Text Generation Dataset

Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das


Abstract
We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.
Anthology ID:
2020.emnlp-main.89
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1173–1186
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.89
DOI:
10.18653/v1/2020.emnlp-main.89
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.89.pdf
Optional supplementary material:
 2020.emnlp-main.89.OptionalSupplementaryMaterial.zip