Post Editing 101

如何提高译后编辑效率?

2019-10-25 18:30 united language group

本文共775个字,阅读需8分钟

阅读模式 切换至中文

Machine Translation (MT) cuts down on cost and turnaround time, thanks to its ability to decipher large amounts of text quickly. The downside to MT is the fact that it doesn’t have the capacity to translate material as a human does, and is not able to pick up on nuance or cultural variations among languages. Because of MT’s inherent shortcomings, it is best if used for material that will only be used internally and won’t be published. With that said , editing MT’s output is a crucial step in making sure your documents are translated correctly, especially if they’ll be client facing. That’s where Post Editing (PE) – the method used to edit machine-translated content – comes in. Because MT technology is still relatively new, there are varied ideas as to how to get the most out of PE. But, there are strategies that can be used to leverage the PE process for stronger translations. Full vs. Light Post Editing PE will often be referred to in two ways: Full Post Editing (FPE) or Light Post Editing (LPE). LPE is usually used if the translated document will only be utilized as a reference material and won’t be published. Light edits consist of fixing blatant errors, but not correcting every minor detail. Stylistic changes won’t be made as the idea behind a quick edit is a faster turnaround. FPE takes a more critical look at a document, preparing it for external use and sometimes publication. A document that has undergone FPE should be completely error-free and maintain stylistic and terminology consistency throughout. The decision whether to use FPE or LPE might depend on the quality of the MT output. If a system creates especially poor MT quality, more editing will be required. On the other hand, some MT systems might only require limited editing. Best Practices Since the impetus behind PE is faster turnaround times, it’s important to have an editor who can make decisions on the fly. It’s also helpful to automate your PE, using a search tool to identify errors that occur more than once. The ability to identify and fix more than one error at a time speeds up the process. In order for the PE process to go smoothly, it’s important to have editors who are experienced with editing MT raw output. In addition, post editors should have subject matter expertise in the area they’re translating for and a strong grasp of the target language. MT is popular for translating technical documents and manuals, and for that reason it helps if editors use a Computer Assisted Translation (CAT) tool during PE. Using a terminology database will help to keep jargon consistent. To streamline the PE process, pre-editing text before it is put through an MT system is beneficial. Making sure formatting is correct and flagging certain segments of text that don’t require translation can eliminate work in PE. With that said, here’s a list of PE strategies: • Think on your feet – Only implement necessary chances, don’t get caught up in minute detail • Automate – Expedite the process by using a CAT tool for a quicker, more effective edit • Pre-edit – Make sure your source text is strong before it’s sent through MT and edited • Set clear guidelines – Make clear what purpose the final machine translated content will serve in order for editors to decide how thorough they should be • Train – Teach linguists not only about PE, but also MT Amidst all the technologies at our fingertips these days, PE emphasizes the fact that the language industry still needs human translators. The fact that MT needs a final read through after it’s been translated is evidence of this. If MT were perfect, we could fully rely on its capabilities without giving it a once over. In a recent New York Times article, Gideon Lewis-Kraus explores the vast possibilities of deep-learning translation, but also points to a machine translated passage of Ernest Hemingway’s “The Snows of Kilimanjaro” that is less than perfect. Using Google Translate’s new neural machine translation, the passage is close to the original, but still lacks human accuracy. As mentioned earlier, raw MT output might not need PE if the translated content won’t be client-facing or used externally. But, if you’re looking to present machine translated material to an audience, it won’t live up to accepted grammatical standards without the help of an editor. It’s no secret that Artificial Intelligence (AI) has progressed amazingly in recent years, but it has yet to outsmart its human counterparts.
机器翻译(MT)能够快速解读大量文本,可以降低成本、缩短翻译周期。但其机器翻译的缺点是其不能像人类那样进行翻译,并且无法体会到不同语言间的细微差别或文化差异。 鉴于其固有的缺陷,机器翻译最好是能够用在内部发行、不公开出版的材料上。因此,编辑机翻文件是确保文件翻译准确性的关键,面对客户时尤为如此。 这便是“译后编辑”(PE, Post Editing)。由于机器翻译技术相对不成熟,如何发挥译后编辑最大效用,人们对此看法不一。但是,有些策略可用于完善译后编辑流程,提升译文质量。 完全译后编辑vs.快速译后编辑 译后编辑通常分两种类型:完全译后编辑 (FPE, Full Post Editing) 或快速译后编辑 (LPE, Light Post Editing)。若翻译的文档仅用作参考资料且不需发行,则通常使用快速译后编辑(LPE)。快速译后编辑会修改明显错误,但不会纠正每个次要细节。由于快速译后编辑的目的是缩短翻译周期,所以不会更改其风格特征 完全译后编辑会对文档进行更为严格的审查,以备对外使用。经过完全译后编辑处理的文档应该完全没有错误,且风格和术语始终保持一致。 完全译后编辑还是快速译后编辑,也取决于机翻译文的质量。如果机器翻译系统给出的译文质量特别差,则需要进行更为细致的译后编辑。不过,其他一些机器翻译系统也许不需要大量译后编辑。 最佳实践 由于进行译后编辑是为了缩短翻译周期,所以有一位决策果断的编辑十分重要。而且,使用搜索工具找出重复的错误,将译后编辑过程自动化,这也很重要。一次识别并修复多个错误,能提升译后编辑速度。 为了确保译后编辑流程顺利,经验丰富的译后编辑人员的重要性不言而喻。此外,他们还应具有相关领域的专业知识,并较好地掌握目标语言。 机器翻译常用在翻译技术文档和手册方面,因此,计算机辅助翻译(CAT)工具可以为译后编辑助力。比如,术语库能确保术语的一致性。 将文本放入机器翻译系统之前进行预编辑,有助于简化译后编辑的流程。确保格式正确并标记某些不需要翻译的文本可以减轻译后编辑的工作量。 以下是一些译后编辑的策略: •反应敏捷–只作必要的修改,不要过分在意细节 •自动化–使用CAT工具加快速度,以高效高速地进行编辑 •预编辑–在将原文放入机器翻译系统之前,先编辑原文。 •制定明确的准则–明确机翻文本的服务对象,以便译后编辑人员对译文进行不同程度的编辑处理。 •培训–让语言人员不仅掌握译后编辑技巧,还要了解机器翻译。 在各种技术触手可及的今天,译后编辑的存在强调这样一个事实:语言行业仍然需要人工翻译。机器翻译最终需要人工编辑便证明了这点。倘若机器翻译的质量堪称完美,那么就不需要译后编辑,人们可以完全依赖机器了。 吉迪恩•刘易斯•克劳斯(Gideon Lewis-Kraus)在《纽约时报》最近刊登的一篇文章中探索了深度学习翻译的巨大可能性。但文中也指出了机器翻译欧内斯特•海明威(Ernest Hemingway)的《乞力马扎罗山的雪》的选段质量仍不完美。虽然使用了谷歌最新的神经机器翻译系统,译文非常接近原文,但仍不如人工翻译那么准确。 如前所述,如果翻译后的内容不面向客户或不在外部使用,那么机器翻译的译文不一定需要译后编辑。但是,如果想公开使用机器翻译的材料,那么在没有译后编辑的情况下,译文可能达不到公认的语法标准 人工智能(AI)在近些年来取得了惊人的进步,这是有目共睹的,但想要超过人类,仍为时尚早。 译后编辑:杨安训(中山大学)

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。

阅读原文