RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes
Semih Yagcioglu, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis
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.- Anthology ID:
- D18-1166
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1358–1368
- Language:
- URL:
- https://aclanthology.org/D18-1166
- DOI:
- 10.18653/v1/D18-1166
- Cite (ACL):
- Semih Yagcioglu, Aykut Erdem, Erkut Erdem, and Nazli Ikizler-Cinbis. 2018. RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1358–1368, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes (Yagcioglu et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1166.pdf
- Data
- RecipeQA, FigureQA, MovieQA, SQuAD, TQA