GEM/totto

Tasks

• table-to-text

Citation text from the card

@inproceedings{parikh-etal-2020-totto,
    title = "{ToTTo}: A Controlled Table-To-Text Generation Dataset",
    author = "Parikh, Ankur  and
      Wang, Xuezhi  and
      Gehrmann, Sebastian  and
      Faruqui, Manaal  and
      Dhingra, Bhuwan  and
      Yang, Diyi  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.89",
    doi = "10.18653/v1/2020.emnlp-main.89",
    pages = "1173--1186",
    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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