With the rapid advancement of Neural Machine Translation (NMT), enhancing
translation efficiency and quality has become a focal point of research.
Despite the commendable performance of general models such as the Transformer
in various aspects, they still fall short in processing long sentences and
fully leveraging bidirectional contextual information. This paper introduces an
improved model based on the Transformer, implementing an asynchronous and
segmented bidirectional decoding strategy aimed at elevating translation
efficiency and accuracy. Compared to traditional unidirectional translations
from left-to-right or right-to-left, our method demonstrates heightened
efficiency and improved translation quality, particularly in handling long
sentences. Experimental results on the IWSLT2017 dataset confirm the
effectiveness of our approach in accelerating translation and increasing
accuracy, especially surpassing traditional unidirectional strategies in long
sentence translation. Furthermore, this study analyzes the impact of sentence
length on decoding outcomes and explores the model's performance in various
scenarios. The findings of this research not only provide an effective encoding
strategy for the NMT field but also pave new avenues and directions for future
studies.

该研究通过改进基于 Transformer 的异步分段双向解码策略，以提高翻译效率和准确性。实验结果表明，在处理长句子方面，与传统的单向翻译方法相比，我们的方法表现出更高的效率和改善的翻译质量。此外，研究还分析了句子长度对解码结果的影响，并探讨了模型在不同场景中的性能。这项研究的发现不仅为 NMT 领域提供了一种有效的编码策略，而且为未来的研究开辟了新的途径和方向。

机器翻译的异步和分段双向编码

Asynchronous and Segmented Bidirectional Encoding for NMT

The dominant neural machine translation (NMT) models apply unified
attentional encoder-decoder neural networks for translation. Traditionally, the
NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a
left-toright manner, leaving the target-side contexts generated from right to
left unexploited during translation. In this paper, we equip the conventional
attentional encoder-decoder NMT framework with a backward decoder, in order to
explore bidirectional decoding for NMT. Attending to the hidden state sequence
produced by the encoder, our backward decoder first learns to generate the
target-side hidden state sequence from right to left. Then, the forward decoder
performs translation in the forward direction, while in each translation
prediction timestep, it simultaneously applies two attention models to consider
the source-side and reverse target-side hidden states, respectively. With this
new architecture, our model is able to fully exploit source- and target-side
contexts to improve translation quality altogether. Experimental results on
NIST Chinese-English and WMT English-German translation tasks demonstrate that
our model achieves substantial improvements over the conventional NMT by 3.14
and 1.38 BLEU points, respectively. The source code of this work can be
obtained from this https URL

本文提出了一种基于双向解码器的神经机器翻译模型，其中正向解码器按原有的方法工作，但加入了逆向解码器，以利用源语言和目标语言的双向信息，从而提高翻译质量。实验结果表明，该模型在中英文和英德文翻译任务上均能够显著提高 NMT 的效果。