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。