  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.
