Amazon Looks to Further Automate Quality Checks in Subtitle Translation

亚马逊希望进一步实现字幕翻译质检自动化

2021-04-07 11:50 slator

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Companies operating in the digital entertainment space have come up with some interesting innovations to reduce production costs. One area they have focused on is dubbing, which offers great potential for increasing the market share of such streaming platforms as Netflix, HBO, and Amazon Prime. Among these innovations, Synthesia’s lip-sync dubbing tech and Papercup’s synthetic dubbing tool stand out in recent memory. Of course, there is also subtitling — which is the use case of a paper published by Amazon researchers on April 1, 2021. Authored by Prabhakar Gupta, Ridha Juneja, Anil Nelakanti, and Tamojit Chatterjee, “Detecting over/under-translation [OT/UT] errors for determining adequacy in human translations” proposes a new approach to flagging errors during the quality evaluation of translated subtitles. The group did not limit their research to machine translated (MT) output, but also specifically targeted instances with professional subtitlers in the translation pipeline. “The goal of our system is to identify OT/UT errors from human translated video subtitles with high error recall,” they said. Moreover, according to the authors, their model was able to detect OT/UT in human translations “without any access to reference translations” — that is, they trained the model on synthetic data. The researchers added that this dataset of “synthetically introduced errors” performed well, “achieving 89.3% accuracy on high-quality human-annotated evaluation data in 8 languages.” Defining translation quality as capturing “both the fluency of the translation and its adequacy relative to the source,” the researchers also raise the possibility of reducing production costs by flagging errors very early on. They wrote, “Translated subtitles often require human quality checks that are as expensive as acquiring translations […] To reduce post-editing quality checks costs, we could flag errors as the translations are typed in with the QE serving as a guardrail.” They compare this system to apps that flag spelling or grammatical errors on the fly. Of course, the kind of translation tech the authors describe is nothing new (see: predictive / adaptive machine translation via Lilt). However, not all MT quality checks are created equal — and what may be unacceptable for, say, translated marketing copy could very well work for subtitles. “For video subtitles […] it is possible for a translation to be linguistically incomplete and be acceptable during post-edits,” the authors pointed out. “This is due to the fact subtitles are required to follow a set of technical constraints limiting the choice and number of words in translation.” They cite an example (“There is a green tree in the park” translated into “Green tree in park”) as passing a subbing quality check because a viewer would understand the context. The Amazon researchers concluded by saying that they still plan to work on their model by “improving error patterns through tighter coupling with human translators” and by limiting errors to tokens within a sentence instead of flagging the entire sentence.
在数字娱乐空间运营的公司想出了一些有趣的创新,以降低生产成本。他们关注的一个领域是配音,这为Netflix,HBO和Amazon Prime等流媒体平台的市场份额提供了巨大的潜力。 在这些创新中,Synthesia的假唱配音技术和PaperCup的合成配音工具在最近脱颖而出。当然,还有字幕领域--这是亚马逊研究人员在2021年4月1日发表的一篇论文的用例。 由Prabhakar Gupta,Ridha Juneja,Anil Nelakanti和Tamojit Chatterjee合著的“检测过度/欠翻译[OT/UT]错误以确定人类翻译的充分性”一书提出了一种在字幕翻译质量评估中标记错误的新方法。 该小组并没有将他们的研究局限于机器翻译(MT)输出,还特别针对翻译系列中带有专业字幕的实例。“我们小组的目标是以高错误召回率从人类翻译的视频字幕中识别OT/UT错误,”他们说。 此外,根据作者的说法,他们的模型能够检测人类翻译中的OT/UT,“而不需要任何参考性翻译”--也就是说,他们根据合成数据训练模型。研究人员补充说,这个“综合引入错误”的数据集表现良好,“在8种语言的高质量人工注释评估数据上达到了89.3%的准确率。” 研究人员认为翻译质量高低的标准是“译文的流畅性和相对于原文的真实性”,并认为通过及早标记错误来降低生产成本具有可能性。 他们写道:“翻译后的字幕通常需要人工质量检查,这与翻译字幕一样昂贵[…]为了降低编辑后的质量检查成本,我们可以在输入翻译时标记错误,而QE则起到了护栏的作用。” 他们将这一系统与那些及时标记拼写或语法错误的应用程序进行了比较。当然,作者所描述的翻译技术并不是什么新技术(参见:通过Lilt的预测/自适应机器翻译)。然而,并不是所有的MT质量检查都是平等的--比如,翻译的营销文案可能无法接受的东西很可能在字幕上奏效。 “对于视频字幕[…],翻译在语言上可能是不完整的,但在后期编辑时是可以接受的,”作者指出。“这是因为字幕需要遵循一套技术约束,限制翻译时字幕的选择和字数。” 他们举了一个通过子质量检查的例子,即使这样翻译观众也会理解上下文,即将“公园里有一棵葱绿的树”翻译成“公园有绿树。 亚马逊的研究人员总结说,他们仍然计划通过“通过与人类翻译器更紧密的耦合来改进错误模式”以及将错误限制在句子中的标记而不是标记整个句子来改进他们的模型。

以上中文文本为机器翻译,存在不同程度偏差和错误,请理解并参考英文原文阅读。

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