A Neuro-Symbolic Approach to Monitoring Salt Content in Food
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909156202512384 |
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| author | Tayal, Anuja Di Eugenio, Barbara Salunke, Devika Boyd, Andrew D. Dickens, Carolyn A Abril, Eulalia P Garcia-Bedoya, Olga Allen-Meares, Paula G |
| author_facet | Tayal, Anuja Di Eugenio, Barbara Salunke, Devika Boyd, Andrew D. Dickens, Carolyn A Abril, Eulalia P Garcia-Bedoya, Olga Allen-Meares, Paula G |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01182 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Neuro-Symbolic Approach to Monitoring Salt Content in Food Tayal, Anuja Di Eugenio, Barbara Salunke, Devika Boyd, Andrew D. Dickens, Carolyn A Abril, Eulalia P Garcia-Bedoya, Olga Allen-Meares, Paula G Computation and Language Symbolic Computation 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. |
| title | A Neuro-Symbolic Approach to Monitoring Salt Content in Food |
| topic | Computation and Language Symbolic Computation |
| url | https://arxiv.org/abs/2404.01182 |