Iterative Multi-document Neural Attention for Multiple Answer Prediction

TL;DR

This paper introduces a neural network model designed to answer questions with multiple answers by leveraging multiple facts from a knowledge base, aiming to enhance personalized, natural language conversational systems.

Contribution

The paper presents a novel neural attention-based model for multi-answer question answering using multiple facts, evaluated on bAbI Movie Dialog tasks.

Findings

Effective in multi-answer question answering

Improves personalized conversational recommendations

Demonstrates strong performance on bAbI dataset

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.