# RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes Source: [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:55:49.570Z Authors: Semih Yagcioglu, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis Published: 2018-09-04T07:04:55.000Z ## Abstract Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text. In this work, we introduce RecipeQA, a dataset for multimodal comprehension of cooking recipes. It comprises of approximately 20K instructional recipes with multiple modalities such as titles, descriptions and aligned set of images. With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge. Our preliminary results indicate that RecipeQA will serve as a challenging test bed and an ideal benchmark for evaluating machine comprehension systems. The data and leaderboard are available at http://hucvl.github.io/recipeqa. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Procedural Multimodal Machine Comprehension on RecipeQA (test)](): Procedural Multimodal Machine Comprehension; RecipeQA (test); Cloze: 27.35. - [Procedural Multimodal Machine Comprehension on CraftQA (test)](): Procedural Multimodal Machine Comprehension; CraftQA (test); Cloze: 27.81. ## Original sources and implementation [Original paper]() [PDF]()