Data-Driven Never-Ending Learning Question Answering Systems

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This tutorial explores two research areas, namely Never-Ending Learning (NEL) and Question Answering (QA). NEL systems [2] are, in a very high-level, computer systems that learn over time to become better in solving a task. Different NEL approaches have been proposed and applied in different tasks and domains, with results that are not yet generalizable to every domain, but encourage us to keep addressing the problem of how to build computer systems that can take advantage of NEL principles. It is not always so straightforward to have NEL principles applied to ML models. In this tutorial, we want to show (with hands-on examples and supporting theory, algorithms, and models) how to model a problem in a NEL fashion and help KDD community to become familiar with such approaches.

The presence of many questions answering systems in our daily life (such as IBM Watson, Amazon Alexa, Apple Siri, MS Cortana, Google Home, etc.), and the recent release of new and bigger datasets focused on Open Domain Question Answering, have contributed to an increased interest on Question Answering and systems that can perform it. But, in spite of advances from the last years, Open Domain Question Answering models cannot yet achieve results comparable to human performance. Thus, Open Domain QA tends to be a good candidate to be modeled in a NEL approach. This tutorial aims at enabling the attendees to:

  1.   Better understand the current state-of-the-art on NEL and QA.
  2.   Learn how to model an ML problem using a NEL approach.
  3.   Be prepared to follow along with NEL-QA ideas and propose new approaches to boost the performance of QA systems.



Estevam Hruschka is a Staff Research Scientist at Megagon Labs. Prior to Megagon Labs, he was co-leader on the conception, design, creation, deployment, and development of the first Never-Ending Learning System in the history of Computer Science and Artificial Intelligence. Also, he was an associate professor of computer science at the Federal University of Sao Carlos (Brazil) and a Visiting Professor at Carnegie Mellon University (Pittsburgh, PA). His work focuses on both theoretical and applied problems and he is mainly interested in how to build intelligent computer systems capable of deeply understanding Natural Language in a Never-Ending Learning approach. Between 2017 and 2020, he was with Amazon (Alexa Search Team) in Seattle, WA.