Data-Driven Never-Ending Learning Question Answering Systems

This tutorial focuses on how to build Question Answering (QA) syetems based on the Never-Ending Learning (NEL) approach. NEL systems can be roughly described as computer systems that learn over time to become better in solving a specific task. Different NEL approaches have been proposed and applied in different tasks and domains. Recent advances encourage us to keep addressing the problem of how to build computer systems that can take advantage of NEL principles. Considering that it is not always so straightforward to have NEL principles applied to ML models, this tutorial guides the audience (with hands-on examples and supporting theory, algorithms and models) on how to model a system in a NEL fashion and intends to help KDD community to become familiar with such approaches. Question Answering is chosen as application domain mainly because of the relevance of the topic (QA) for KDD and AI communities in general.