论文标题
联合培训真的有助于级联的语音翻译吗？
Does Joint Training Really Help Cascaded Speech Translation?
论文作者
论文摘要
目前，在语音翻译中，直接的方法 - 级联具有翻译系统的识别系统 - 提供最新的结果。但是，仍然存在基本挑战，例如自动语音识别系统的错误传播等基本挑战。为了减轻这些问题，人们将注意力转向直接数据并提出各种联合培训方法。在这项工作中，我们试图回答一个问题，即联合培训是否确实有助于级联的语音翻译。我们回顾了有关该主题的最新论文，并通过将转录后验概率边缘化来调查联合培训标准。我们的发现表明，强大的级联基线可以减少使用联合培训获得的任何改进，我们建议替代联合培训。我们希望这项工作可以作为当前语音翻译景观的进修，并激励研究更有效，更具创造性的方法来利用直接数据进行语音翻译。
Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results. However, fundamental challenges such as error propagation from the automatic speech recognition system still remain. To mitigate these problems, recently, people turn their attention to direct data and propose various joint training methods. In this work, we seek to answer the question of whether joint training really helps cascaded speech translation. We review recent papers on the topic and also investigate a joint training criterion by marginalizing the transcription posterior probabilities. Our findings show that a strong cascaded baseline can diminish any improvements obtained using joint training, and we suggest alternatives to joint training. We hope this work can serve as a refresher of the current speech translation landscape, and motivate research in finding more efficient and creative ways to utilize the direct data for speech translation.
