BERT Pre-training for Cooking Time Prediction from Japanese Cooking Recipes

抄録

Recently, websites that enable users to share and search for cooking recipes have gained popularity. Each recipe typically includes various pieces of information, including a title, a list of ingredients, and detailed steps described in text and illustrated with photos. The estimated cooking time for each recipe is another valuable information when selecting a recipe. However, it can be difficult to accurately determine cooking time because it depends on various factors, such as heat level, ingredient quantity, and cooking skill level. Therefore, some recipes do not include information on cooking time. In this study, we consider the prediction of cooking time in general scenarios based on a list of ingredients and a textual description of each recipe's cooking process using BERT, a natural language processing model. To this end, we propose an additional pretraining method that assigns greater weight to words related to cooking time using a cooking ontology. Our experimental results show that our method outperforms a fine-tuned BERT model with additional pre-training using a commonly employed approach. Notably, words representing “Kitchen Tools” are particularly associated with cooking time.