Iterative Multi-document Neural Attention for Multiple Answer Prediction
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 to support users in their information seeking processes in a personalized way.
Cite
@article{arxiv.1702.02367,
title = {Iterative Multi-document Neural Attention for Multiple Answer Prediction},
author = {Claudio Greco and Alessandro Suglia and Pierpaolo Basile and Gaetano Rossiello and Giovanni Semeraro},
journal= {arXiv preprint arXiv:1702.02367},
year = {2017}
}
Comments
Paper accepted and presented at the Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents (URANIA) workshop, held in the context of the AI*IA 2016 conference