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。

通过二分搜索学习同时机器翻译的最优策略

Learning Optimal Policy for Simultaneous Machine Translation via Binary  Search

Simultaneous text translation and end-to-end speech translation have recently
made great progress but little work has combined these tasks together. We
investigate how to adapt simultaneous text translation methods such as wait-k
and monotonic multihead attention to end-to-end simultaneous speech translation
by introducing a pre-decision module. A detailed analysis is provided on the
latency-quality trade-offs of combining fixed and flexible pre-decision with
fixed and flexible policies. We also design a novel computation-aware latency
metric, adapted from Average Lagging.

本文调查了如何将同步文本翻译方法与同时语音翻译相结合，并通过引入预决定模块来适应于端到端的同时语音翻译。同时，对将固定和灵活的预决策与固定和灵活的策略相结合的延迟质量权衡进行了详细分析。作者还设计了一种新的计算感知的延迟度量方法，该方法从平均滞后进行调整。

从 SimulMT 到 SimulST: 将同时翻译文本转换为端到端同时翻译语音

SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End  Simultaneous Speech Translation

Simultaneous machine translation begins to translate each source sentence
before the source speaker is finished speaking, with applications to live and
streaming scenarios. Simultaneous systems must carefully schedule their reading
of the source sentence to balance quality against latency. We present the first
simultaneous translation system to learn an adaptive schedule jointly with a
neural machine translation (NMT) model that attends over all source tokens read
thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention,
which maintains both a hard, monotonic attention head to schedule the reading
of the source sentence, and a soft attention head that extends from the
monotonic head back to the beginning of the source. We show that MILk's
adaptive schedule allows it to arrive at latency-quality trade-offs that are
favorable to those of a recently proposed wait-k strategy for many latency
values.

我们使用 Monotonic Infinite Lookback（MILk）注意力机制来同时翻译，并学习一种自适应调度模式，以平衡质量和延迟，并展示其与最近提出的延迟策略相比，更有利于延迟 - 质量权衡。