@inproceedings{tayal-etal-2024-neuro,
    title = "A Neuro-Symbolic Approach to Monitoring Salt Content in Food",
    author = "Tayal, Anuja  and
      Di Eugenio, Barbara  and
      Salunke, Devika  and
      Boyd, Andrew D.  and
      Dickens, Carolyn A.  and
      Abril, Eulalia P.  and
      Garcia-Bedoya, Olga  and
      Allen-Meares, Paula G.",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.11",
    pages = "93--103",
    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.",
}

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.
Tayal, Anuja Di Eugenio, Barbara Salunke, Devika Boyd, Andrew D. Dickens, Carolyn A. Abril, Eulalia P. Garcia-Bedoya, Olga Allen-Meares, Paula G.
A Neuro-Symbolic Approach to Monitoring Salt Content in Food