RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
By Michał Bień, M. Gilski, M. Maciejewska, W. Taisner, Dawid Wisniewski, Agnieszka Lawrynowicz. 2020
81 citations recorded by the source corpus.
Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG – a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Noah A. Smith
- Elizabeth Clark
- Suchin Gururangan
- Yejin Choi
- Sofia Serrano
- Yoav Goldberg
- Percy Liang
- Mohit Bansal
In Natural Language Processing
- Elizabeth Clark
- Noah A. Smith
- Suchin Gururangan
- Sofia Serrano
- Yejin Choi
- Percy Liang
- Graham Neubig
- Yonatan Belinkov
In Evaluation & Benchmarks
- Elizabeth Clark
- Noah A. Smith
- Suchin Gururangan
- Yejin Choi
- Yonatan Belinkov
- Mohit Bansal
- Jordan L. Boyd-Graber
- Swabha Swayamdipta
Citations in the corpus
- All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021, methodology): “We collected 50 human-authored recipes from the RecipeNLG dataset (Bie ń et al., 2020), which contains 2,231,142 recipes scraped from the web.”
- LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “Furthermore, we demonstrate that our LongForm models – instruction-tuned on LongForm-C– outperform prior instruction-tuned baselines such as FLAN-T5 and Alpaca by a big margin on a wide variety of long text generation tasks.”
- CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation (2024, unknown): “RecipeGen: We use RecipeNLG (Bie ´ n et al., 2020) and select 25,000 samples from the training split.”