Attention-based Ingredient Phrase Parser

TL;DR

This paper introduces an attention-based ingredient phrase parser that accurately extracts structured ingredient information from unstructured recipe texts, achieving state-of-the-art performance with over 0.93 F1-score.

Contribution

The paper presents a novel attention-based model for ingredient parsing that significantly improves extraction accuracy from unstructured recipe data.

Findings

Achieves over 0.93 F1-score on benchmark datasets

Outperforms existing methods in ingredient attribute extraction

Provides a robust solution for structured ingredient information retrieval

Abstract

As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation. Assisting users to cook is one of these tasks that are expected to be solved by intelligent assistants, where ingredients and their corresponding attributes, such as name, unit, and quantity, should be provided to users precisely and promptly. However, existing ingredient information scraped from the cooking website is in the unstructured form with huge variation in the lexical structure, for example, '1 garlic clove, crushed', and '1 (8 ounce) package cream cheese, softened', making it difficult to extract information exactly. To provide an engaged and successful conversational service to users for cooking tasks, we propose a new ingredient parsing model that can parse an ingredient phrase of recipes into the structure form with its corresponding attributes with over 0.93 F1-score. Experimental results show that our model achieves state-of-the-art performance on AllRecipes and Food.com datasets.