A popular approach to streaming speech translation is to employ a single
offline model with a \textit{wait-$k$} policy to support different latency
requirements, which is simpler than training multiple online models with
different latency constraints. However, there is a mismatch problem in using a
model trained with complete utterances for streaming inference with partial
input. We demonstrate that speech representations extracted at the end of a
streaming input are significantly different from those extracted from a
complete utterance. To address this issue, we propose a new approach called
Future-Aware Streaming Translation (FAST) that adapts an offline ST model for
streaming input. FAST includes a Future-Aware Inference (FAI) strategy that
incorporates future context through a trainable masked embedding, and a
Future-Aware Distillation (FAD) framework that transfers future context from an
approximation of full speech to streaming input. Our experiments on the MuST-C
EnDe, EnEs, and EnFr benchmarks show that FAST achieves better trade-offs
between translation quality and latency than strong baselines. Extensive
analyses suggest that our methods effectively alleviate the aforementioned
mismatch problem between offline training and online inference.

我们提出了一种名为 FAST 的方法，它可以为流输入调整离线 ST 模型，包括将未来信息集成到推理策略中和将未来信息从全语音的近似传输到流输入的框架，从而解决了离线训练和在线推理之间的不匹配问题，并在 MuST-C EnDe，EnEs 和 EnFr 基准测试中比强基线模型实现更好的平衡翻译质量和延迟。

使用未来感知蒸馏和推理对离线语音翻译模型进行流式处理的改进

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference

In simultaneous translation (SimulMT), the most widely used strategy is the
wait-k policy thanks to its simplicity and effectiveness in balancing
translation quality and latency. However, wait-k suffers from two major
limitations: (a) it is a fixed policy that can not adaptively adjust latency
given context, and (b) its training is much slower than full-sentence
translation. To alleviate these issues, we propose a novel and efficient
training scheme for adaptive SimulMT by augmenting the training corpus with
adaptive prefix-to-prefix pairs, while the training complexity remains the same
as that of training full-sentence translation models. Experiments on two
language pairs show that our method outperforms all strong baselines in terms
of translation quality and latency.

本文提出一种用于自适应 SimulMT 训练的有效方案，通过向训练语料库中添加自适应前缀对前缀的语句，来缓解 Wait-K 策略的两个主要限制，实验证明该方法在翻译质量和延迟方面优于所有强基准线。

数据驱动的自适应同时机器翻译

Data-Driven Adaptive Simultaneous Machine Translation

Beam search is universally used in full-sentence translation but its
application to simultaneous translation remains non-trivial, where output words
are committed on the fly. In particular, the recently proposed wait-k policy
(Ma et al., 2019a) is a simple and effective method that (after an initial
wait) commits one output word on receiving each input word, making beam search
seemingly impossible. To address this challenge, we propose a speculative beam
search algorithm that hallucinates several steps into the future in order to
reach a more accurate decision, implicitly benefiting from a target language
model. This makes beam search applicable for the first time to the generation
of a single word in each step. Experiments over diverse language pairs show
large improvements over previous work.

本文研究了如何在同时翻译领域使用 Beam Search 算法，提出了一种基于预测的 Speculative Beam Search 算法，经过实验证实其有效性。

推测性波束搜索用于同时翻译

Speculative Beam Search for Simultaneous Translation

Simultaneous translation, which translates sentences before they are
finished, is useful in many scenarios but is notoriously difficult due to
word-order differences. While the conventional seq-to-seq framework is only
suitable for full-sentence translation, we propose a novel prefix-to-prefix
framework for simultaneous translation that implicitly learns to anticipate in
a single translation model. Within this framework, we present a very simple yet
surprisingly effective wait-k policy trained to generate the target sentence
concurrently with the source sentence, but always k words behind. Experiments
show our strategy achieves low latency and reasonable quality (compared to
full-sentence translation) on 4 directions: zh<->en and de<->en.

该研究提出了一种基于前缀的预测模型，采用 wait-k 策略来实现当前缀的翻译，并在中英文和德英文四个方向上实现了较低的延迟和合理的质量。