Long Short-Term Memory-Networks for Machine Reading.

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.The system is initially designed to process a single sequence but we also demonstrate how to integrate it with an encoder-decoder architecture.Experiments on language modeling, sentiment analysis, and natural language inference show that our model matches or outperforms the state of the art.