RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation

Michał Bień, Michał Gilski, Martyna Maciejewska, Wojciech Taisner, Dawid Wisniewski, Agnieszka Lawrynowicz

Abstract
Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG – a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.
Anthology ID:
2020.inlg-1.4
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Editors:
Brian Davis, Yvette Graham, John Kelleher, Yaji Sripada
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–28
Language:
URL:
https://aclanthology.org/2020.inlg-1.4
DOI:
10.18653/v1/2020.inlg-1.4
Bibkey:
Cite (ACL):
Michał Bień, Michał Gilski, Martyna Maciejewska, Wojciech Taisner, Dawid Wisniewski, and Agnieszka Lawrynowicz. 2020. RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation. In Proceedings of the 13th International Conference on Natural Language Generation, pages 22–28, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Bień et al., INLG 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.inlg-1.4.pdf
Data
RecipeNLG
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