People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. In a scenario in which the user profile can be considered as a question, intelligent agents able to answer questions can be used to find the most relevant answers for a given user. In this work we propose a novel model based on Artificial Neural Networks to answer questions with multiple answers by exploiting multiple facts retrieved from a knowledge base. The model is evaluated on the factoid Question Answering and top-n recommendation tasks of the bAbI Movie Dialog dataset. After assessing the performance of the model on both tasks, we try to define the long-Term goal of a conversational recommender system able to interact using natural language and supporting users in their information seeking processes in a personalized way.
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|Titolo:||Iterative multi-document neural attention for multiple answer prediction|
|Data di pubblicazione:||2017|
|Citazione:||Iterative multi-document neural attention for multiple answer prediction / Greco, Claudio; Suglia, Alessandro; Basile, Pierpaolo; Rossiello, Gaetano; Semeraro, Giovanni. - 1802(2017), pp. 19-29. ((Intervento presentato al convegno 2016 AI*IA Workshop on Deep Understanding and Reasoning: A Challenge for Next-Generation Intelligent Agents, URANIA 2016 tenutosi a ita nel 2016.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|