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
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2018-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.