2023/06/20 by Saed Rezayi, Rezayi, Saed, Zhengliang Liu +19 · 1 citation
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Computation and Language (cs.CL) #Culinary Culture and Tourism #FOS: Computer and information sciences #Nutrition, Genetics, and Disease
paper · pdf · doi:10.48550/arxiv.2306.11892
openalex publication_date 2023/06/20 · openalex created_date 2023/06/24 · openalex updated_date 2026/07/28
This paper explores new frontiers in agricultural natural language processing by investigating the effectiveness of using food-related text corpora for pretraining transformer-based language models. In particular, we focus on the task of semantic matching, which involves establishing mappings between food descriptions and nutrition data. To accomplish this, we fine-tune a pre-trained transformer-based language model, AgriBERT, on this task, utilizing an external source of knowledge, such as the FoodOn ontology. To advance the field of agricultural NLP, we propose two new avenues of exploration: (1) utilizing GPT-based models as a baseline and (2) leveraging ChatGPT as an external source of knowledge. ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships. Additionally, we experiment with other applications, such as cuisine prediction based on food ingredients, and expand the scope of our research to include other NLP tasks beyond semantic matching. Overall, this paper provides promising avenues for future research in this field, with potential implications for improving the performance of agricultural NLP applications.