Flavor is expressed through interaction of molecules via gustatory and olfactory mechanisms. Knowing the utility of flavor molecules in food and fragrances, it is valuable to add a comprehensive repository of flavor compounds characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis. FlavorDB2 (https://cosylab.iiitd.edu.in/flavordb2/) is an updated database of flavor molecules with an user-friendly interface. This repository simplifies the search for flavor molecules, their attributes and offers a range of applications including food pairing. FlavorDB2 serves as a standard repository of flavor compounds.

Here we explore the problem of speech video interpolation. With close to 70% of web traffic, such content today forms the primary form of online communication and entertainment. Despite high performance on conventional metrics like MSE, PSNR, and SSIM, we find that the state-of-the-art frame interpolation models fail to produce faithful speech interpolation. For instance, we observe the lips stay static while the person is still speaking for most interpolated frames. With this motivation, using the information of words, subwords, and visemes, we provide a new set of linguistically informed metrics targeted explicitly to the problem of speech video interpolation. We release several datasets to test video interpolation models of their speech understanding. We also design linguistically informed deep learning video interpolation algorithms to generate the missing frames.

Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately. We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. The Ingredients section in a recipe typically lists down the ingredients required and corresponding attributes such as quantity, temperature, and processing state. This can be modelled by defining these attributes and their values. The physical entities which make up a recipe can be broadly classified into utensils, ingredients and their combinations that are related by cooking techniques. The instruction section lists down a series of events in which a cooking technique or process is applied upon these utensils and ingredients. We model these relationships in the form of tuples. Thus, using a combination of these methods we model cooking recipe in the dataset RecipeDB [1] to show the efficacy of our method. This mined information model can have several applications which include translating recipes between languages, determining similarity between recipes, generation of novel recipes and estimation of the nutritional profile of recipes. For the purpose of recognition of ingredient attributes, we train the Named Entity Relationship (NER) models and analyze the inferences with the help of K-Means clustering. Our model presented with an F1 score of 0.95 across all datasets. We use a similar NER tagging model for labelling cooking techniques (F1 score = 0.88) and utensils (F1 score = 0.90) within the instructions section. Finally, we determine the temporal sequence of relationships between ingredients, utensils and cooking techniques for modeling the instruction steps.

The availability of an accurate nutrition profile of recipes is an important feature for food databases with several applications including nutritional assistance, recommendation systems, and dietary analytics. Often in online databases, recipes are obtained from diverse sources in an attempt to maximize the number of recipes and variety of the dataset. This leads to an incomplete and often unreliable set of nutritional details. We propose a scalable method for nutritional profile estimation of recipes from their ingredients section using a standard reliable database for the nutritional values. Previous studies have testified the efficiency of string-matching methods on small datasets. To demonstrate the effectiveness of our procedure, we apply the proposed method on a large dataset, RecipeDB [1], which contains recipes from multiple data sources, using the United States Department of Agriculture Standard Reference (USDASR) Database as a reference for computing nutritional profiles. We evaluate our method by calculating the average error across our database of recipes (36 calories per serving) which is well within the range of errors attributable to physical variations.

Named entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a lot of named entities — viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while most occur rarely. In this paper, we build upon the previous work done on language models developed for text with named entities by introducing a Hierarchically Disentangled Model. Training is divided into multiple branches with each branch producing a model with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the performance of the model. Hence, we have curated 158,473 cooking recipes from several publicly available online sources. To reliably derive the entities within this corpus, we employ a combination of Named Entity Recognition (NER) as well as an unsupervised method of interpretation using dependency parsing and POS tagging, followed by a further cleaning of the dataset. This unsupervised interpretation models instructions as action graphs and is specific to the corpus of cooking recipes, unlike NER which is a general method applicable to all corpora. To delve into the utility of our language model, we apply it to tasks such as graph-to-text generation and ingredients-to-recipe generation, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations and processed action graphs) available for use, considering their potential use cases for language modeling and text generation research.

Abstract Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment. As we stand on the verge of an epidemic of diet-linked disorders, it is eminently important to investigate the culinary correlates of recipes to probe their association with sensory responses as well as consequences for nutrition and health. RecipeDB (https://cosylab.iiitd.edu.in/recipedb) is a structured compilation of recipes, ingredients and nutrition profiles interlinked with flavor profiles and health associations. The repertoire comprises of meticulous integration of 118 171 recipes from cuisines across the globe (6 continents, 26 geocultural regions and 74 countries), cooked using 268 processes (heat, cook, boil, simmer, bake, etc.), by blending over 20 262 diverse ingredients, which are further linked to their flavor molecules (FlavorDB), nutritional profiles (US Department of Agriculture) and empirical records of disease associations obtained from MEDLINE (DietRx). This resource is aimed at facilitating scientific explorations of the culinary space (recipe, ingredient, cooking processes/techniques, dietary styles, etc.) linked to taste (flavor profile) and health (nutrition and disease associations) attributes seeking for divergent applications. Database URL: https://cosylab.iiitd.edu.in/recipedb