非分段输入的同声翻译：一种滑动窗口方法
Simultaneous Translation for Unsegmented Input: A Sliding Window Approach
摘要：
在口语翻译（口语翻译）的级联方法中，ASR输出通常在传递给MT之前被标点和分割成句子，因为后者通常是在书面文本上训练的。然而，由于ASR系统的错误的结尾标点，错误的分段导致翻译质量的下降，特别是在输入不断更新的同时（在线）设置中。为了减少自动分割的影响，我们提出了一种滑动窗口的方法来翻译原始ASR输出（在线或离线），而不需要依赖于自动分割器。我们使用从原始训练数据中提取的并行窗口（而不是并行句子）来训练翻译模型。在测试时，我们在窗口级别进行翻译，并使用一种简单的方法连接翻译后的窗口以生成最终的翻译。对英语到德语和英语到捷克语的实验表明，我们的方法比通常的ASR分割器管道提高了1.3- 2.0 BLEU点，并且与基于基线参考的在线翻译系统相比，固定长度的窗口大大减少了闪烁。
英文摘要：
In the cascaded approach to spoken language translation (SLT), the ASR output is typically punctuated and segmented into sentences before being passed to MT, since the latter is typically trained on written text. However, erroneous segmentation, due to poor sentence-final punctuation by the ASR system, leads to degradation in translation quality, especially in the simultaneous (online) setting where the input is continuously updated. To reduce the influence of automatic segmentation, we present a sliding window approach to translate raw ASR outputs (online or offline) without needing to rely on an automatic segmenter. We train translation models using parallel windows (instead of parallel sentences) extracted from the original training data. At test time, we translate at the window level and join the translated windows using a simple approach to generate the final translation. Experiments on English-to-German and English-to-Czech show that our approach improves 1.3--2.0 BLEU points over the usual ASR-segmenter pipeline, and the fixed-length window considerably reduces flicker compared to a baseline retranslation-based online SLT system.
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