Ratatouille: A tool for Novel Recipe Generation
Mansi Goel, Pallab Chakraborty, Vijay Ponnaganti, Minnet Khan,, Sritanaya Tatipamala, Aakanksha Saini, Ganesh Bagler

TL;DR
This paper introduces Ratatouille, a web-based tool that leverages deep learning models like LSTMs and GPT-2 trained on extensive recipe data to generate realistic, novel cooking recipes, advancing the field of natural language processing in culinary innovation.
Contribution
The paper presents a novel web application that uses deep learning models trained on large recipe datasets to generate new, realistic recipes.
Findings
Deep learning models can generate plausible new recipes.
Ratatouille demonstrates practical application of NLP in culinary creativity.
The tool is accessible via a user-friendly web interface.
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.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Web Data Mining and Analysis
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Dropout · Residual Connection · Attention Dropout · Dense Connections · Byte Pair Encoding · Cosine Annealing · Discriminative Fine-Tuning
