We present trellis networks, a new architecture for sequence modeling. On the
one hand, a trellis network is a temporal convolutional network with special
structure, characterized by weight tying across depth and direct injection of
the input into deep layers. On the other hand, we show that truncated recurrent
networks are equivalent to trellis networks with special sparsity structure in
their weight matrices. Thus trellis networks with general weight matrices
generalize truncated recurrent networks. We leverage these connections to
design high-performing trellis networks that absorb structural and algorithmic
elements from both recurrent and convolutional models. Experiments demonstrate
that trellis networks outperform the current state of the art methods on a
variety of challenging benchmarks, including word-level language modeling and
character-level language modeling tasks, and stress tests designed to evaluate
long-term memory retention. The code is available at
this https URL .

提出了一种新的序列建模架构：活动网络，其是具有特殊结构的时间卷积网络；文章表明，具有特殊稀疏结构的截断循环网络等效于具有一般重量矩阵的活动网络。实验表明，活动网络在词级语言建模和字符级语言建模任务等多个基准上均优于现有技术。