Iterative Multi-document Neural Attention for Multiple Answer Prediction

Claudio Greco, Alessandro Suglia, Pierpaolo Basile, Gaetano Rossiello, Giovanni Semeraro

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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 to support users in their information seeking processes in a personalized way.
Original languageEnglish
Pages (from-to)19-29
Number of pages11
JournalCEUR Workshop Proceedings
Publication statusPublished - 2 Mar 2017
EventAI*IA Workshop on Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents 2016 - Genova, Italy
Duration: 28 Nov 201628 Nov 2016


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