ToTTo: A Controlled Table-To-Text Generation Dataset

• URL: http://arxiv.org/abs/2004.14373v3
• Date: Tue, 6 Oct 2020 06:07:06 GMT
• Title: ToTTo: A Controlled Table-To-Text Generation Dataset
• Authors: Ankur P. Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
• Abstract summary: ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples. We introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. While usually fluent, existing methods often hallucinate phrases that are not supported by the table.
• Score: 61.83159452483026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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