This paper proposes a decoding strategy for end-to-end simultaneous speech translation. We leverage end-to-end models trained in offline mode and conduct an empirical study for two language pairs (English-to-German and English-to-Portuguese). We also investigate different output token granularities including characters and Byte Pair Encoding (BPE) units. The results show that the proposed decoding approach allows to control BLEU/Average Lagging trade-off along different latency regimes. Our best decoding settings achieve comparable results with a strong cascade model evaluated on the simultaneous translation track of IWSLT 2020 shared task.

中文翻译：

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端到端同时语音翻译解码策略的实证研究

本文提出了一种端到端同时语音翻译的解码策略。我们利用在离线模式下训练的端到端模型，对两种语言对（英语到德语和英语到葡萄牙语）进行了实证研究。我们还研究了不同的输出令牌粒度，包括字符和字节对编码（BPE）单位。结果表明，所提出的解码方法可以控制BLEU /平均滞后权衡沿不同的等待时间制度。我们的最佳解码设置通过在IWSLT 2020共享任务的同时转换轨迹上评估的强大级联模型获得了可比的结果。