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.

Iterative multi-document neural attention for multiple answer prediction

BASILE, PIERPAOLO;ROSSIELLO, GAETANO;SEMERARO, Giovanni
2017-01-01

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/194878
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