@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Neuro-Symbolic Approach to Monitoring Salt Content in Food
%A Tayal, Anuja
%A Di Eugenio, Barbara
%A Salunke, Devika
%A Boyd, Andrew D.
%A Dickens, Carolyn A.
%A Abril, Eulalia P.
%A Garcia-Bedoya, Olga
%A Allen-Meares, Paula G.
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tayal-etal-2024-neuro
%X 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.
%U https://aclanthology.org/2024.cl4health-1.11
%P 93-103
Markdown (Informal)
[A Neuro-Symbolic Approach to Monitoring Salt Content in Food](https://aclanthology.org/2024.cl4health-1.11) (Tayal et al., CL4Health-WS 2024)
ACL
- Anuja Tayal, Barbara Di Eugenio, Devika Salunke, Andrew D. Boyd, Carolyn A. Dickens, Eulalia P. Abril, Olga Garcia-Bedoya, and Paula G. Allen-Meares. 2024. A Neuro-Symbolic Approach to Monitoring Salt Content in Food. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 93–103, Torino, Italia. ELRA and ICCL.