---
title: "Cultural Adaptation of Recipes — paper by Cao et al. (2024)"
description: "The authors define cross-cultural recipe adaptation between Mandarin Chinese and English-speaking… 70 references, cited by 35 papers here."
authors: ["Yong Cao", "Yova Kementchedjhieva", "Ruixiang Cui", "Antonia Karamolegkou", "Li Zhou", "Megan Dare", "Lucia Donatelli", "Daniel Hershcovich"]
year: 2024
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2024.tacl-1.5/"
doi: "10.1162/tacl_a_00634"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cao-2024-cultural-adaptation-recipes/"
---
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# Cultural Adaptation of Recipes

**Authors:** [Yong Cao](https://md.research.elamur.ai/free/en/authors/yong-cao/), [Yova Kementchedjhieva](https://md.research.elamur.ai/free/en/authors/yova-kementchedjhieva/), [Ruixiang Cui](https://md.research.elamur.ai/free/en/authors/ruixiang-cui/), [Antonia Karamolegkou](https://md.research.elamur.ai/free/en/authors/antonia-karamolegkou/), [Li Zhou](https://md.research.elamur.ai/free/en/authors/li-zhou/), Megan Dare, [Lucia Donatelli](https://md.research.elamur.ai/free/en/authors/lucia-donatelli/) and [Daniel Hershcovich](https://md.research.elamur.ai/free/en/authors/daniel-hershcovich/) · **Year:** 2024 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2024](https://md.research.elamur.ai/free/en/venues/tacl/2024/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.tacl-1.5/) · [PDF](https://aclanthology.org/2024.tacl-1.5.pdf) · [DOI 10.1162/tacl\_a\_00634](https://doi.org/10.1162/tacl_a_00634) · [OpenAlex](https://openalex.org/W4391494825)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · [35](https://md.research.elamur.ai/free/en/papers/cao-2024-cultural-adaptation-recipes/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Recipes are procedural texts that depend on culturally specific knowledge: which ingredients are common, how quantities are expressed, what equipment a kitchen has, and the order in which steps are done. Standard machine translation tends to miss these aspects and produces literal or confusing output. The authors define cross-cultural recipe adaptation between Mandarin Chinese and English-speaking cuisines as a new generation task. They build CulturalRecipes by pairing dishes from two independent monolingual corpora. Titles are machine-translated into the other language and embedded, and recipes with sufficiently similar titles are treated as the same dish. This yields noisy silver training data in both directions, plus a small human-written gold test set. They compare retrieval, machine translation, finetuned multilingual sequence-to-sequence models and LLMs such as GPT-4, using automatic metrics and human judgments. Finetuned models learn to produce adapted, faithful recipes, but LLMs without task-specific training score higher. GPT-4 is strong for Chinese→English but still falls short of human adapters for English→Chinese. The dataset serves as a benchmark for culturally aware generation.

## Key points
- **Source corpora:** RecipeNLG contains over 2M English recipes and extends Recipe1M and Recipe1M+. XiaChuFang contains 1.5M recipes from xiachufang.com. Only the title, ingredients and steps fields are used.
- **Matching procedure:** each source title retrieves up to k = 10 nearest neighbours, and candidates below a cosine similarity of 0.85 are discarded.
  - The zh→en train/validation split has about 44.5k source recipes with 144.6k matched targets.
  - The en→zh split has about 43.8k sources with 120.7k targets.
- **Cultural contrasts analysed:**
  - Ingredients: "red beans" in Chinese recipes correspond to adzuki beans in Western ones.
  - Units: Chinese recipes give vague amounts, while US recipes use cups and quarts.
  - Tools: Chinese recipes favour the stovetop, English ones the oven.
  - Techniques: blanching is common in Chinese recipes and rare in English ones.
- **GPT-4 outputs:** GPT-4 turns imprecise Chinese amounts into explicit quantities and offers alternatives for distinctly Chinese ingredients. Finetuned models sometimes struggle with coherence across recipe components.
- **Availability:** the data, code and trained models are released at github.com/coastalcph/cultural-recipes.

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

## Institutions

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- [Huazhong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/huazhong-university-of-science-and-technology/) — University, Wuhan, China · 45 works · 6,345 citations
- [University of Electronic Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) — University, Chengdu, China · 31 works · 2,012 citations

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## Cite

Cite as: Yong Cao, Yova Kementchedjhieva, Ruixiang Cui, Antonia Karamolegkou, Li Zhou, Megan Dare, Lucia Donatelli and Daniel Hershcovich. 2024. Cultural Adaptation of Recipes. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{cao2024cultural,
  title = {{Cultural Adaptation of Recipes}},
  author = {Yong Cao, Yova Kementchedjhieva, Ruixiang Cui, Antonia Karamolegkou, Li Zhou, Megan Dare, Lucia Donatelli and Daniel Hershcovich},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2024},
  doi = {10.1162/tacl\_a\_00634},
  url = {https://doi.org/10.1162/tacl_a_00634}
}
```

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*Updated 30 October 2026*