In this paper, we describe our submission to the Simultaneous Speech
Translation at IWSLT 2022. We explore strategies to utilize an offline model in
a simultaneous setting without the need to modify the original model. In our
experiments, we show that our onlinization algorithm is almost on par with the
offline setting while being $3\times$ faster than offline in terms of latency
on the test set. We also show that the onlinized offline model outperforms the
best IWSLT2021 simultaneous system in medium and high latency regimes and is
almost on par in the low latency regime. We make our system publicly available.

本文介绍了我们在 IWSLT 2022 Simultaneous Speech Translation 比赛中的参赛作品，探讨了如何在不修改原始模型的情况下，在同步设置中利用离线模型的策略。在实验中，我们展示了我们的在线算法几乎与离线设置相等，而在测试集上的延迟方面比离线快 3 倍。我们还展示了在线化的离线模型在中高延迟区间比最佳 IWSLT2021 同声翻译系统表现更好，在低延迟区间则几乎持平。我们的系统已经公开发布。

IWSLT 2022 年同声传译任务 CUNI-KIT 系统

CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022

In simultaneous speech translation (SimulST), finding the best trade-off
between high translation quality and low latency is a challenging task. To meet
the latency constraints posed by the different application scenarios, multiple
dedicated SimulST models are usually trained and maintained, generating high
computational costs. In this paper, motivated by the increased social and
environmental impact caused by these costs, we investigate whether a single
model trained offline can serve not only the offline but also the simultaneous
task without the need for any additional training or adaptation. Experiments on
en->{de, es} indicate that, aside from facilitating the adoption of
well-established offline techniques and architectures without affecting
latency, the offline solution achieves similar or better translation quality
compared to the same model trained in simultaneous settings, as well as being
competitive with the SimulST state of the art.

本文研究如何在同时翻译中平衡高翻译质量和低延迟之间的权衡，并探讨通过训练单个离线模型来解决多个同时翻译模型维护造成的高计算成本的问题。研究结果表明，离线模型实现了与同时训练模型相似或更好的翻译质量，并且具有更低的延迟和更高的可扩展性。

同声传译是否需要同时模型？

Does Simultaneous Speech Translation need Simultaneous Models?

We consider the problem of answering queries about a sensitive dataset
subject to differential privacy. The queries may be chosen adversarially from a
larger set Q of allowable queries in one of three ways, which we list in order
from easiest to hardest to answer:
Offline: The queries are chosen all at once and the differentially private
mechanism answers the queries in a single batch.
Online: The queries are chosen all at once, but the mechanism only receives
the queries in a streaming fashion and must answer each query before seeing the
next query.
Adaptive: The queries are chosen one at a time and the mechanism must answer
each query before the next query is chosen. In particular, each query may
depend on the answers given to previous queries.
Many differentially private mechanisms are just as efficient in the adaptive
model as they are in the offline model. Meanwhile, most lower bounds for
differential privacy hold in the offline setting. This suggests that the three
models may be equivalent.
We prove that these models are all, in fact, distinct. Specifically, we show
that there is a family of statistical queries such that exponentially more
queries from this family can be answered in the offline model than in the
online model. We also exhibit a family of search queries such that
exponentially more queries from this family can be answered in the online model
than in the adaptive model. We also investigate whether such separations might
hold for simple queries like threshold queries over the real line.

探讨在三种模式下对不对称敏感数据集的查询差分隐私问题，证明这些模式不同，并且存在家族类的统计查询，在离线模式下可以回答指数级别的查询，而在线模式下只能回答很少的查询，同时也展示了一族搜索查询，在在线模式下可以回答指数级别的查询，但在调整模式下无法回答很多查询。