Papers › ToTTo: A Controlled Table-To-Text Generation Dataset

ToTTo: A Controlled Table-To-Text Generation Dataset

29 Apr 2020EMNLP 2020 11arXiv:2004.14373archive 2025-07-28

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

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.

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Tasks

Conditional Text GenerationData-to-Text GenerationSentenceTable-to-Text GenerationText Generation

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ToTTo

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Data-to-Text Generation ToTTo BERT-to-BERT BLEU 44 #3 of 6 Archive leaderboard report
Data-to-Text Generation ToTTo BERT-to-BERT PARENT 52.6 #3 of 6 Archive leaderboard report
Data-to-Text Generation ToTTo Pointer Generator BLEU 41.6 #4 of 6 Archive leaderboard report
Data-to-Text Generation ToTTo Pointer Generator PARENT 51.6 #4 of 6 Archive leaderboard report
Data-to-Text Generation ToTTo NCP+CC (Puduppully et al 2019) BLEU 19.2 #5 of 6 Archive leaderboard report
Data-to-Text Generation ToTTo NCP+CC (Puduppully et al 2019) PARENT 29.2 #5 of 6 Archive leaderboard report

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