Simultaneous Machine Translation (SiMT) generates translation while reading
source tokens, essentially producing the target prefix based on the source
prefix. To achieve good performance, it leverages the relationship between
source and target prefixes to exact a policy to guide the generation of
translations. Although existing SiMT methods primarily focus on the
Encoder-Decoder architecture, we explore the potential of Decoder-only
architecture, owing to its superior performance in various tasks and its
inherent compatibility with SiMT. However, directly applying the Decoder-only
architecture to SiMT poses challenges in terms of training and inference. To
alleviate the above problems, we propose the first Decoder-only SiMT model,
named Decoder-only Streaming Transformer (DST). Specifically, DST separately
encodes the positions of the source and target prefixes, ensuring that the
position of the target prefix remains unaffected by the expansion of the source
prefix. Furthermore, we propose a Streaming Self-Attention (SSA) mechanism
tailored for the Decoder-only architecture. It is capable of obtaining
translation policy by assessing the sufficiency of input source information and
integrating with the soft-attention mechanism to generate translations.
Experiments demonstrate that our approach achieves state-of-the-art performance
on three translation tasks.

在这篇论文中，我们介绍了一种名为 Decoder-only Streaming Transformer (DST) 的 Decoder-only SiMT 模型，该模型通过编码源前缀和目标前缀的位置来确保目标前缀的位置不受源前缀扩展的影响，并通过 Streaming Self-Attention 机制获取翻译策略来生成翻译，实验证明我们的方法在三个翻译任务中实现了最先进的性能。

仅解码流式变换器用于同声传译

Decoder-only Streaming Transformer for Simultaneous Translation

Simultaneous machine translation (SiMT) starts to output translation while
reading the source sentence and needs a precise policy to decide when to output
the generated translation. Therefore, the policy determines the number of
source tokens read during the translation of each target token. However, it is
difficult to learn a precise translation policy to achieve good latency-quality
trade-offs, because there is no golden policy corresponding to parallel
sentences as explicit supervision. In this paper, we present a new method for
constructing the optimal policy online via binary search. By employing explicit
supervision, our approach enables the SiMT model to learn the optimal policy,
which can guide the model in completing the translation during inference.
Experiments on four translation tasks show that our method can exceed strong
baselines across all latency scenarios.

本文提出了一种利用二分查找在线构建最佳策略的方法来实现并行句子翻译的优化，通过显式监管来实现最优策略的学习，从而在所有延迟情况下超越了强基线。