Long Short-Term Memory-Networks for Machine Reading

Code

• github.com/muu4649/word2vec_attention
  tf★ 0
• github.com/oneil512/lstmn
  pytorch★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions