SHARE: a System for Hierarchical Assistive Recipe Editing
arXiv:2105.08185
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.
Presented at EMNLP 2022 main conference
References in corpus (6)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- CTRL: A Conditional Transformer Language Model for Controllable Generation
- Language Models as Knowledge Bases?
- Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation
- Generating Personalized Recipes from Historical User Preferences