42是面向字幕的语音翻译的全部答案吗？
Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?
摘要：
由于每天都有大量视听内容，字幕对传播信息越来越重要。虽然神经机器翻译（NMT）可以加快翻译视听内容的过程，但是仍然需要大量的手动工作来转录源语言，以及将文本定位和分割成适当的字幕。在时间和分段方面创建适当的字幕在很大程度上取决于音频中存在的信息（话语持续时间，自然停顿）。在这项工作中，我们探讨了两种方法应用语音翻译（ST）的字幕：a）直接端到端和b）一个经典的级联方法。我们讨论的好处，有机会获得的源语言的语音，以提高所生成的字幕的空间和时间的字幕限制的一致性，并表明，长度是不是一切的情况下，面向字幕ST的答案。
英文摘要：
Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles. Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses). In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach. We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length is not the answer to everything in the case of subtitling-oriented ST.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
