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://aclanthology.org/2020.emnlp-main.89
- DOI:
- 10.18653/v1/2020.emnlp-main.89
- Cite (ACL):
- Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020. ToTTo: A Controlled Table-To-Text Generation Dataset. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1173–1186, Online. Association for Computational Linguistics.
- Cite (Informal):
- ToTTo: A Controlled Table-To-Text Generation Dataset (Parikh et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.emnlp-main.89.pdf
- Code
- google-research-datasets/ToTTo
- Data
- ToTTo, RotoWire, WikiBio