Blockwise self-attentional encoder models have recently emerged as one
promising end-to-end approach to simultaneous speech translation. These models
employ a blockwise beam search with hypothesis reliability scoring to determine
when to wait for more input speech before translating further. However, this
method maintains multiple hypotheses until the entire speech input is consumed
-- this scheme cannot directly show a single \textit{incremental} translation
to users. Further, this method lacks mechanisms for \textit{controlling} the
quality vs. latency tradeoff. We propose a modified incremental blockwise beam
search incorporating local agreement or hold-$n$ policies for quality-latency
control. We apply our framework to models trained for online or offline
translation and demonstrate that both types can be effectively used in online
mode.
Experimental results on MuST-C show 0.6-3.6 BLEU improvement without changing
latency or 0.8-1.4 s latency improvement without changing quality.

通过修改增量性的分块束搜索策略，本文提出了一种用于控制质量 - 延迟权衡的方法，并应用于在线和离线翻译模型，实验结果表明在 MuST-C 数据集上 BLEU 指标提升 0.6-3.6，延迟降低 0.8-1.4 秒。

增量式分块波束搜索实现同时语音翻译与可控质量 - 延迟折衷

Incremental Blockwise Beam Search for Simultaneous Speech Translation  with Controllable Quality-Latency Tradeoff

Simultaneous Machine Translation is the task of incrementally translating an
input sentence before it is fully available. Currently, simultaneous
translation is carried out by translating each sentence independently of the
previously translated text. More generally, Streaming MT can be understood as
an extension of Simultaneous MT to the incremental translation of a continuous
input text stream. In this work, a state-of-the-art simultaneous sentence-level
MT system is extended to the streaming setup by leveraging the streaming
history. Extensive empirical results are reported on IWSLT Translation Tasks,
showing that leveraging the streaming history leads to significant quality
gains. In particular, the proposed system proves to compare favorably to the
best performing systems.

该研究拓展了现有的同声翻译系统，提出一种基于流历史的流机器翻译方法，取得了较大的质量提升，并且表现优于现有同类最佳系统。