Search Results for author: Donghyeon Park

Found 4 papers, 3 papers with code

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

no code implementations1 May 2024 Donghee Choi, Mogan Gim, Donghyeon Park, Mujeen Sung, Hyunjae Kim, Jaewoo Kang, Jihun Choi

This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired.

Descriptive Language Modelling +1

KitchenScale: Learning to predict ingredient quantities from recipe contexts

1 code implementation21 Apr 2023 Donghee Choi, Mogan Gim, Samy Badreddine, Hajung Kim, Donghyeon Park, Jaewoo Kang

We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context.

Language Modelling Transfer Learning

RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

1 code implementation14 Oct 2022 Mogan Gim, Donghee Choi, Kana Maruyama, Jihun Choi, Hajung Kim, Donghyeon Park, Jaewoo Kang

To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients.

KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks

1 code implementation16 May 2019 Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, Jaewoo Kang

As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers.

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