A Neuro-Symbolic Approach to Monitoring Salt Content in Food

• URL: http://arxiv.org/abs/2404.01182v1
• Date: Mon, 1 Apr 2024 15:34:24 GMT
• Title: A Neuro-Symbolic Approach to Monitoring Salt Content in Food
• Authors: Anuja Tayal, Barbara Di Eugenio, Devika Salunke, Andrew D. Boyd, Carolyn A Dickens, Eulalia P Abril, Olga Garcia-Bedoya, Paula G Allen-Meares,
• Abstract summary: We propose a dialogue system that enables heart failure patients to inquire about salt content in foods. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a template-based conversational dataset. 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.
• Score: 1.4347098305628967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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