We present a conversational system that aims at calculating the amount of consumed carbohydrates in a meal by diabetic patients. Through a chat input, users can freely describe foods, which are first semantically interpreted and then matched against a nutritionist database for the final calculation of carbohydrates. Specific issues that have been addressed include: large-scale food recognition in Italian, without any restriction; interpretation of fuzzy quantities in relation to food (e.g. a portion of, a dish of, etc.); exploitation of dialogue strategies to revise system mis-interpretations and failures. CH1 integrates innovative neural approaches to language interpretation with rule-based approaches for ontology reasoning. In the paper we provide both experimental evaluations for the main components of the system, as well as qualitative user tests.

CH1: A Conversational System to Calculate Carbohydrates in a Meal

Bernardo Magnini;Vevake Balaraman;Mauro Dragoni;Marco Guerini;Simone Magnolini
;
2018

Abstract

We present a conversational system that aims at calculating the amount of consumed carbohydrates in a meal by diabetic patients. Through a chat input, users can freely describe foods, which are first semantically interpreted and then matched against a nutritionist database for the final calculation of carbohydrates. Specific issues that have been addressed include: large-scale food recognition in Italian, without any restriction; interpretation of fuzzy quantities in relation to food (e.g. a portion of, a dish of, etc.); exploitation of dialogue strategies to revise system mis-interpretations and failures. CH1 integrates innovative neural approaches to language interpretation with rule-based approaches for ontology reasoning. In the paper we provide both experimental evaluations for the main components of the system, as well as qualitative user tests.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/317910
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