  We introduce a new paradigm of learning for reasoning, understanding, and
prediction, as well as the scaffolding network to implement this paradigm. The
scaffolding network embodies an incremental learning approach that is
formulated as a teacher-student network architecture to teach machines how to
understand text and do reasoning. The key to our computational scaffolding
approach is the interactions between the teacher and the student through
sequential questioning. The student observes each sentence in the text
incrementally, and it uses an attention-based neural net to discover and
register the key information in relation to its current memory. Meanwhile, the
teacher asks questions about the observed text, and the student network gets
rewarded by correctly answering these questions. The entire network is updated
continually using reinforcement learning. Our experimental results on synthetic
and real datasets show that the scaffolding network not only outperforms
state-of-the-art methods but also learns to do reasoning in a scalable way even
with little human generated input.
