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home / papers / 2026 / JFC-Recipe: A Dataset for Nutrient Estimation from Japanese User-Generated Cooking Recipes

JFC-Recipe: A Dataset for Nutrient Estimation from Japanese User-Generated Cooking Recipes

Summary

Calculating the nutritional content of home-cooking recipes requires two things: mapping each ingredient to an entry in a nutrient database, and working out how many grams of it are used. User-generated recipes make both steps hard because ingredient names and quantities are written inconsistently. Until now, no fully hand-annotated resource covered both steps, and none existed for Japanese. The authors present JFC-Recipe, built on 1,985 recipes from the Cookpad dataset. Each ingredient is manually linked to an item in the government-maintained Standards Tables of Food Composition in Japan, which lists nutrients per 100 g. Annotators also recorded each ingredient's amount and unit as separate fields. A new weight table converts those amount–unit pairs into grams. The paper reports annotation procedure, statistics and inter-annotator agreement. It then frames nutrient estimation as two subtasks, food item estimation and quantity estimation, and tests pre-trained language model baselines. These baselines perform accurately and improve as training data grows. The resource supports dietary-control applications and adds a non-English counterpart to datasets such as Recipe1M+.

Key points

  • Data: 1,985 Japanese recipes from Cookpad (Harashima et al., 2016). Ingredients are mapped to the Japanese food composition table, which defines 2,538 food items with hierarchical names and per-100 g values for calories, PRO, FAT, CHO, minerals and vitamins.
  • Annotation layers:
    • Ingredient-to-food-item linking.
    • Amount and unit tagged separately (e.g., "2" + "本").
    • A weight table that maps food item and unit to grams.
  • Comparison with Recipe1M+: Recipe1M+ links ingredients to USDA SR27 (8,618 items) by automatic text matching and extracts quantities with patterns. JFC-Recipe is fully manual and covers 666 distinct food items versus 357 in Recipe1M+.
  • Novelty claim: First Japanese recipe dataset combining ingredient nutrients with amount-and-unit quantities. The quantity work is related to KitchenScale (Choi et al., 2023).
  • Experiments: Pre-trained language models are applied to both subtasks and show gains as training data increases. The extracted text omits the exact scores and agreement figures.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/42sopxjrdzsf

@inproceedings{shirai2026jfc,
  title = {{JFC-Recipe: A Dataset for Nutrient Estimation from Japanese User-Generated Cooking Recipes}},
  author = {Shirai, Keisuke and Yamakata, Yoko and Kameko, Hirotaka and Sunto, Akiko and Harashima, Jun and Mori, Shinsuke},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/42sopxjrdzsf},
  url = {https://doi.org/10.63317/42sopxjrdzsf}
}

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