RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes
TL
TL;DR
This work introduces RecipeQA, a dataset for multimodal comprehension of cooking recipes, 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.
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.
References40 items
1
The NarrativeQA Reading Comprehension Challenge
2
Simulating Action Dynamics with Neural Process Networks
3
FigureQA: An Annotated Figure Dataset for Visual Reasoning
4
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
5