Long Short-Term Memory-Networks for Machine Reading

Understand

In this paper we address the question of how to render sequence-level networks better at handling structured input.

• We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and attention.
• The reader extends the Long Short-Term Memory architecture with a memory network in place of a single memory cell.
• This enables adaptive memory usage during recurrence with neural attention, offering a way to weakly induce relations among tokens.