We propose MRU (Multi-Range Reasoning Units), a new fast compositional
encoder for machine comprehension (MC). Our proposed MRU encoders are
characterized by multi-ranged gating, executing a series of parameterized
contract-and-expand layers for learning gating vectors that benefit from long
and short-term dependencies. The aims of our approach are as follows: (1)
learning representations that are concurrently aware of long and short-term
context, (2) modeling relationships between intra-document blocks and (3) fast
and efficient sequence encoding. We show that our proposed encoder demonstrates
promising results both as a standalone encoder and as well as a complementary
building block. We conduct extensive experiments on three challenging MC
datasets, namely RACE, SearchQA and NarrativeQA, achieving highly competitive
performance on all. On the RACE benchmark, our model outperforms DFN (Dynamic
Fusion Networks) by 1.5%-6% without using any recurrent or convolution layers.
Similarly, we achieve competitive performance relative to AMANDA on the
SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any
LSTM/GRU layers. Finally, incorporating MRU encoders with standard BiLSTM
architectures further improves performance, achieving state-of-the-art results.

本文介绍了一种新型的机器理解编码器 MRU，具有多级门控、并行端到端的学习门控向量、适用于短期和长期依赖关系三个特点，为机器理解提供高效处理文档、优化模型性能、建模文档内部关系的方法。通过在三个具有挑战性的 MC 数据集上进行广泛的实验，我们证明了该编码器表现出了极具竞争力的性能表现与机器编码器和组件模块，是一种高性能状态的机器理解编码器。