@inproceedings{li-etal-2022-share,
title = "{SHARE}: a System for Hierarchical Assistive Recipe Editing",
author = "Li, Shuyang and
Li, Yufei and
Ni, Jianmo and
McAuley, Julian",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.761",
doi = "10.18653/v1/2022.emnlp-main.761",
pages = "11077--11090",
abstract = "The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset{---}83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints{---}demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.",
}
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<abstract>The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset—83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints—demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.</abstract>
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%0 Conference Proceedings
%T SHARE: a System for Hierarchical Assistive Recipe Editing
%A Li, Shuyang
%A Li, Yufei
%A Ni, Jianmo
%A McAuley, Julian
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-share
%X The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset—83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints—demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.
%R 10.18653/v1/2022.emnlp-main.761
%U https://aclanthology.org/2022.emnlp-main.761
%U https://doi.org/10.18653/v1/2022.emnlp-main.761
%P 11077-11090
Markdown (Informal)
[SHARE: a System for Hierarchical Assistive Recipe Editing](https://aclanthology.org/2022.emnlp-main.761) (Li et al., EMNLP 2022)
ACL
- Shuyang Li, Yufei Li, Jianmo Ni, and Julian McAuley. 2022. SHARE: a System for Hierarchical Assistive Recipe Editing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11077–11090, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.