Cook Smarter Not Harder: Enhancing Learning Capacity in Smart Ovens with Supplementary Data

This paper presents a novel approach to improve cooking experiences by integrating advanced natural language processing and machine learning techniques within the food industry. The research focuses on developing a predictive model that optimally predicts cooking settings for nutrient categorization, cooked product aspect and moisture reduction level. By leveraging NLP techniques, the model aims to interpret and classify recipes across multiple languages, enabling smart ovens to adjust settings autonomously. Joint learning is employed to train a transformer-based model, by combining the losses of the three tasks. Challenges such as multilingual data variability, dataset imbalance and model interpretability are addressed through different methodologies including data augmentation, feature engineering and Explainable AI techniques. The outcomes contribute to improve cooking processes, reducing errors and enhancing user trust in AI -driven culinary solutions. The results indicate that Translate- Train-All translation method, where the training set is balanced across languages, significantly improves the model's performance, by nearly 1.00%, especially for low resource languages. Also, adding the additional genre feature enhances the model's knowledge and improves its predictive capabilities by almost 1.00%.