Anuja Tayal


2024

pdf bib
A Neuro-Symbolic Approach to Monitoring Salt Content in Food
Anuja Tayal | Barbara Di Eugenio | Devika Salunke | Andrew D. Boyd | Carolyn A. Dickens | Eulalia P. Abril | Olga Garcia-Bedoya | Paula G. Allen-Meares
Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024

We propose a dialogue system that enables heart failure patients to inquire about salt content in foods and help them monitor and reduce salt intake. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a template-based conversational dataset. The dataset is structured to ask clarification questions to identify food items and their salt content. Our findings indicate that while fine-tuning transformer-based models on the dataset yields limited performance, the integration of Neuro-Symbolic Rules significantly enhances the system’s performance. Our experiments show that by integrating neuro-symbolic rules, our system achieves an improvement in joint goal accuracy of over 20% across different data sizes compared to naively fine-tuning transformer-based models.