ToTTo: A Controlled Table-To-Text Generation Dataset

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Data-to-Text Generation ToTTo BERT-to-BERT BLEU 44 # 3
PARENT 52.6 # 3
Data-to-Text Generation ToTTo NCP+CC (Puduppully et al 2019) BLEU 19.2 # 5
PARENT 29.2 # 5
Data-to-Text Generation ToTTo Pointer Generator BLEU 41.6 # 4
PARENT 51.6 # 4


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