Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

19 Feb 2015Jason Weston • Antoine Bordes • Sumit Chopra • Alexander M. Rush • Bart van Merriënboer • Armand Joulin • Tomas Mikolov

One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more.

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