Designing a practical, low complexity, close to optimal, channel decoder for
powerful algebraic codes with short to moderate block length is an open
research problem. Recently it has been shown that a feed-forward neural network
architecture can improve on standard belief propagation decoding, despite the
large example space. In this paper we introduce a recurrent neural network
architecture for decoding linear block codes. Our method shows comparable bit
error rate results compared to the feed-forward neural network with
significantly less parameters. We also demonstrate improved performance over
belief propagation on sparser Tanner graph representations of the codes.
Furthermore, we demonstrate that the RNN decoder can be used to improve the
performance or alternatively reduce the computational complexity of the mRRD
algorithm for low complexity, close to optimal, decoding of short BCH codes.

本文设计了一种适用于短到中等块长度的强代数码的实用性低复杂度接近最优通道解码器。我们介绍了一种循环神经网络结构来解码线性分块码，并表明该模式与前馈神经网络模式相比，参数明显更少，具有可比较的误比特率结果，提高了 Tanner 图解码的表现。此外，我们证明，RNN 解码器可用于提高 mRRD 算法的性能或降低计算复杂性，以进行低复杂度、接近最优的短 BCH 码解码。

线性分组码的 RNN 解码

RNN Decoding of Linear Block Codes

We propose to enhance the RNN decoder in a neural machine translator (NMT)
with external memory, as a natural but powerful extension to the state in the
decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At
each time during decoding, \textsc{MemDec} will read from this memory and write
to this memory once, both with content-based addressing. Unlike the unbounded
memory in previous work\cite{RNNsearch} to store the representation of source
sentence, the memory in \textsc{MemDec} is a matrix with pre-determined size
designed to better capture the information important for the decoding process
at each time step. Our empirical study on Chinese-English translation shows
that it can improve by $4.8$ BLEU upon Groundhog and $5.3$ BLEU upon on Moses,
yielding the best performance achieved with the same training set.

通过使用外部内存来增强神经机器翻译中 RNN 解码器的性能，我们提出了一种名为 MemDec 的基于内容寻址的 RNN 解码器。 通过在汉英翻译任务中的实证研究，我们发现 MemDec 相对于 Groundhog 和 Moses，可以提高 4.8 和 5.3 BLEU 分数，从而在相同的训练集上获得最佳性能。