KitchenScale: Learning to predict ingredient quantities from recipe contexts
This work addresses a domain-specific problem for cooking enthusiasts and health-conscious individuals by providing automated ingredient quantity predictions, but it is incremental as it builds on existing pre-trained language models and methods.
The authors tackled the problem of predicting ingredient quantities from recipe contexts by introducing KitchenScale, a fine-tuned pre-trained language model, which demonstrated understanding and generalizability in experiments on a newly constructed dataset.
Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. 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. To effectively train our KitchenScale model, we formulate an ingredient quantity prediction task that consists of three sub-tasks which are ingredient measurement type classification, unit classification, and quantity regression task. Furthermore, we utilized transfer learning of cooking knowledge from recipe texts to PLMs. We adopted the Discrete Latent Exponent (DExp) method to cope with high variance of numerical scales in recipe corpora. Experiments with our newly constructed dataset and recommendation examples demonstrate KitchenScale's understanding of various recipe contexts and generalizability in predicting ingredient quantities. We implemented a web application for KitchenScale to demonstrate its functionality in recommending ingredient quantities expressed in numerals (e.g., 2) with units (e.g., ounce).