There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied on small paired recipe data (e.g., a recipe paired with a similar one that satisfies a dietary constraint). However, pre-trained language models generate inconsistent or incoherent recipes, and paired datasets are not available at scale. We address these deficiencies with RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques. The model is trained for recipe completion to learn semantic relationships within recipes. Our work's main innovation is our unsupervised critiquing module that allows users to edit recipes by interacting with the predicted ingredients; the system iteratively rewrites recipes to satisfy users' feedback. Experiments on the Recipe1M recipe dataset show that our model can more effectively edit recipes compared to strong language-modeling baselines, creating recipes that satisfy user constraints and are more correct, serendipitous, coherent, and relevant as measured by human judges.

翻译：近年来，随着在线食谱数据的可用性提升，自动生成满足特定饮食限制的烹饪食谱引起了广泛关注。已有研究采用预训练语言模型，或依赖小规模配对食谱数据（例如，将食谱与满足饮食约束的类似食谱配对）。然而，预训练语言模型生成的食谱存在不一致或不连贯的问题，而配对数据集也难以大规模获取。为弥补这些不足，我们提出RecipeCrit——一种层次化去噪自编码器，可基于配料层面的批判性意见编辑食谱。该模型通过食谱补全任务训练，以学习食谱内部的语义关联。本研究的主要创新在于无监督批判模块，该模块允许用户通过预测配料交互来编辑食谱；系统迭代重写食谱以响应用户反馈。在Recipe1M食谱数据集上的实验表明，与强语言建模基线相比，我们的模型能更有效地编辑食谱，生成符合用户约束且经人工评估具有更高正确性、偶然性、连贯性与相关性的食谱。