Ratatouille: A tool for Novel Recipe Generation
- URL: http://arxiv.org/abs/2206.08267v1
- Date: Tue, 10 May 2022 11:20:19 GMT
- Title: Ratatouille: A tool for Novel Recipe Generation
- Authors: Mansi Goel, Pallab Chakraborty, Vijay Ponnaganti, Minnet Khan,
Sritanaya Tatipamala, Aakanksha Saini and Ganesh Bagler
- Abstract summary: Ratatouille is a web based application to generate novel recipes.
We trained various Deep Learning models such as LSTMs and GPT-2 with a large amount of recipe data.
- Score: 2.458554997628989
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Due to availability of a large amount of cooking recipes online, there is a
growing interest in using this as data to create novel recipes. Novel Recipe
Generation is a problem in the field of Natural Language Processing in which
our main interest is to generate realistic, novel cooking recipes. To come up
with such novel recipes, we trained various Deep Learning models such as LSTMs
and GPT-2 with a large amount of recipe data. We present Ratatouille
(https://cosylab.iiitd.edu.in/ratatouille2/), a web based application to
generate novel recipes.
Related papers
- Monte Carlo Tree Search for Recipe Generation using GPT-2 [0.8057006406834466]
We propose RecipeMC, a text generation method using GPT-2 that relies on Monte Carlo Tree Search (MCTS)
RecipeMC allows us to define reward functions to put soft constraints on text generation and thus improve the credibility of the generated recipes.
Our results show that human evaluators prefer recipes generated with RecipeMC more often than recipes generated with other baseline methods.
arXiv Detail & Related papers (2024-01-10T14:50:46Z) - Large Language Models as Sous Chefs: Revising Recipes with GPT-3 [56.7155146252028]
We focus on recipes as an example of complex, diverse, and widely used instructions.
We develop a prompt grounded in the original recipe and ingredients list that breaks recipes down into simpler steps.
We also contribute an Amazon Mechanical Turk task that is carefully designed to reduce fatigue while collecting human judgment of the quality of recipe revisions.
arXiv Detail & Related papers (2023-06-24T14:42:43Z) - Counterfactual Recipe Generation: Exploring Compositional Generalization
in a Realistic Scenario [60.20197771545983]
We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient.
We collect a large-scale recipe dataset in Chinese for models to learn culinary knowledge.
Results show that existing models have difficulties in modifying the ingredients while preserving the original text style, and often miss actions that need to be adjusted.
arXiv Detail & Related papers (2022-10-20T17:21:46Z) - Towards Fine-Dining Recipe Generation with Generative Pre-trained
Transformers [1.167576384742479]
We propose a novel way of creating new, fine-dining recipes from scratch using Transformers.
Given a small dataset of food recipes, we try to train models to identify cooking techniques, propose novel recipes, and test the power of fine-tuning with minimal data.
arXiv Detail & Related papers (2022-09-26T15:33:09Z) - Cross-lingual Adaptation for Recipe Retrieval with Mixup [56.79360103639741]
Cross-modal recipe retrieval has attracted research attention in recent years, thanks to the availability of large-scale paired data for training.
This paper studies unsupervised domain adaptation for image-to-recipe retrieval, where recipes in source and target domains are in different languages.
A novel recipe mixup method is proposed to learn transferable embedding features between the two domains.
arXiv Detail & Related papers (2022-05-08T15:04:39Z) - Structure-Aware Generation Network for Recipe Generation from Images [142.047662926209]
We investigate an open research task of generating cooking instructions based on only food images and ingredients.
Target recipes are long-length paragraphs and do not have annotations on structure information.
We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task.
arXiv Detail & Related papers (2020-09-02T10:54:25Z) - Multi-modal Cooking Workflow Construction for Food Recipes [147.4435186953995]
We build MM-ReS, the first large-scale dataset for cooking workflow construction.
We propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow.
arXiv Detail & Related papers (2020-08-20T18:31:25Z) - Decomposing Generation Networks with Structure Prediction for Recipe
Generation [142.047662926209]
We propose a novel framework: Decomposing Generation Networks (DGN) with structure prediction.
Specifically, we split each cooking instruction into several phases, and assign different sub-generators to each phase.
Our approach includes two novel ideas: (i) learning the recipe structures with the global structure prediction component and (ii) producing recipe phases in the sub-generator output component based on the predicted structure.
arXiv Detail & Related papers (2020-07-27T08:47:50Z) - RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and
Evaluation System [29.150333060513177]
We present RecipeGPT, a novel online recipe generation and evaluation system.
System provides two modes of text generations: instruction generation from given recipe title and ingredients; and ingredient generation from recipe title and cooking instructions.
Back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset.
arXiv Detail & Related papers (2020-03-05T09:25:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.