Technical authoring for maximum translation benefit

让好的技术写作来实现翻译效益最大化

2020-04-26 17:50 Star Transit NXT

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Today’s topic is a slightly unusual one as it’s all about writing for translation. You might already be looking at me with a puzzled expression. Surely, translation happens AFTER you’ve written the text, not before or during?! Well, yes and no. I’ve touched on this before in my blogs about reducing translation costs and understanding translation memory software, as well as the blog about optimising your content for translation. Today I’d like to look at it in slightly more detail, as well as introducing a STAR Group tool that could just simplify your technical writing process. Writing clearly and concisely This should be an obvious one. In any technical writing task, your number one objective should be to write clearly and concisely. Keep your sentences short. Avoid jargon and overcomplicating your subject. This does not change when you are writing your document for translation. It just becomes more important. Looking at potential fuzzy match percentages I discuss fuzzy matches a lot in these posts, and I always try to avoid jargon. However, I appreciate that the whole concept of a fuzzy match might feel like an alien language. Today, I’m going to try and give you an example. Translation memory software works on the basis of analysing similarity between two units of language; usually a sentence. Using a fuzzy logic algorithm, it breaks down the sentence into its component parts, i.e. words, punctuation marks and numbers. It looks at each one of these component parts, checks whether it has moved or disappeared from the sentence and combines the results from each component analysis to create a fuzzy match percentage. That’s about all the explanation I can give you. No, seriously, don’t ask me any further questions on this. Computer science is not my strong point! Plus, every company uses different weightings in the algorithm so they will all get slightly different numbers. So let’s look at an example. Examples: I have a cat; his name is Loki. I have a dog; his name is Rover. My dog’s name is Rover. If we consider the first sentence to be already translated, what do the two examples tell us about potential fuzzy matches? Factually, both subsequent sentences have the same meaning. You possess a dog; you call him Rover. There are two changes between sentence 1 and sentences 2/3: Dog not cat Rover not Loki Comparison of sentence 1 and 2: 75% (classed as a fuzzy match) Comparison of sentence 1 and 3: Less than 30% match (this will be classed as new words) Every agency will have a different breakdown of costs between fuzzy matches and new words, but the principle is the same. New words cost more. If your entire document contains similar issues, costs will be significantly higher than they need to be. Avoiding errors We’re all aware that texts that contain errors are more difficult for the reader. Either grammatical errors in long, tangled sentences, or perhaps a sentence that is littered with typing errors. Both of these also cause issues for the translator. Potentially it is an issue that is amplified by the fact that they are not native speakers of your language and might find it harder to untangle or decipher the mistakes. It might take the translator longer to complete your translation project and they may be less willing to work on your texts in the future. As well as there being a risk that they will misunderstand part of your text. So, how does this affect your costs? I’ve not come across any agency that imposes cost penalties for texts that contain multiple errors, though they may suggest carrying out an additional proofreading step before translation. The costs come from misunderstandings that lead to further proofreading steps and incur additional costs to correct errors. Another concern is for subsequent projects where errors have been corrected. Instead of being able to reuse material as pretranslation, your latest project will be considered as fuzzy matches only. This will add a sizeable percentage increase to your technical translation quotation. Introducing MindReader This blog is not really about selling, so I’ll keep this section brief. Even with the best of intentions, it can be difficult to write consistently. It’s more likely that consistency issues will only be found at a proofreading stage or that they might slip through the net completely. For this reason, the STAR Group has developed authoring tools to help; MindReader and MindReader for Outlook. Like any tool from STAR, the principle is that you only work on content that is new. Think of it a little bit like autocomplete on your mobile. Just start typing your sentence, and the tool will provide suggestions from elsewhere in your document. If you want to reuse them, you can. If you don’t, you can ignore them. It can help with consistency in your technical writing, which will improve clarity as well as bringing down potential translation costs. If it sounds like something you could be interested in, contact one of our team today. I hope this blog has been useful in giving you some tips for improving your technical writing and lowering your translation costs. If you want any further information about this, or to discuss a potential project with one of the team, please do not hesitate to get in touch.
今天的主题有点不同寻常,因为它是关于翻译文本写作的。 你可能已经用迷惑的表情看着我了。 很多人确信翻译发生在写作之后而不是之前或期间?! 不过,这种说法半对半错。 我以前在关于降低翻译成本和理解翻译记忆软件,以及关于优化翻译内容的博客中,都谈到过这个问题。 今天,我将稍微详细地介绍一下这个问题,并介绍一个STAR Group的工具,它可以简化你的技术写作过程。 简明扼要地写作 这一点应该是显而易见的。 在任何技术写作任务中,你的首要目标应该是保证写作简明扼要。 句子要简短,要避免行话和过于复杂的主题。 在编写需要翻译的文档时也要简明扼要。 而且,在这个过程中,这一点更为重要。 查看潜在的模糊匹配百分比 我在这些帖子里讨论了很多有关模糊匹配的话题,讨论时我总是尽量避免使用行话。 然而,在我看来,模糊匹配的整个概念可能感觉像是一种外星人的语言。 今天,我要试着给大家举个例子。 翻译记忆软件是在分析两种语言单位相似性的基础上工作的,这种语言单位通常是一个句子。 它使用模糊逻辑算法,将句子分解成各个组成部分,如单词、标点符号和数字。 翻译记忆软件查看句子的每一个组成部分,检查各部分是否改变了在句中的位置或缺失,并结合每个组成部分分析的结果来创建模糊匹配百分比。 我只能给你这么多解释了。 而且,说真的,别再问我这个问题了。 计算机科学不是我的强项! 另外,每家公司在算法中使用不同的权重,所以不同软件算出的数字都会略有不同。 让我们来看一个例子。 示例: 我有一只猫; 它叫洛基。 我有一条狗; 它叫罗弗。 我的狗叫罗弗。 如果我们认为第一句已经被翻译了,那么这两个例子告诉了我们关于潜在模糊匹配的什么信息? 事实上,后面两句话的意思是一样的。 你拥有一条狗;你叫它罗弗。 第1句和第2、3句之间有两个变化: 狗不是猫 罗弗不是洛基 句子1和句子2的比较结果:75%(归类为模糊匹配) 句子1和句子3的比较结果:不到30%的匹配(这将被归类为生词) 每个机构会在模糊匹配和新词之间作出不同的成本细分,但原理是一样的。 生词成本更高。 如果你的整个文档都包含类似的问题,那么成本将大大高于原本需要的成本。 避免错误 我们都知道,包含错误信息的文本对读者来说更难读。不论是长难句中的语法错误,还是一个充满打字错误的句子,都会影响阅读。 这两个问题也会给翻译人员带来麻烦,而且难度会升级,因为他们不是你的母语使用者,可能会觉得更难识别或更正错误。 译者可能需要花更长的时间来完成你的翻译项目,而且他们以后可能不太愿意翻译你的文本。 此外,他们可能会误解你文本中的部分内容。 那么,这对你的成本有什么影响呢? 虽然机构可能会建议在翻译之前对文本进行额外的校对,但我还没有见过任何机构会对包含多处错误的文本处以成本罚款。 成本由误解造成,由于误解的存在,文本需要进一步校对,因此会产生额外的纠正错误的成本。 另一个值得关注的问题是,错误已被纠正的后续项目。 不同于在预翻译时能够重新使用的材料,修改后的最新项目仅被认为是模糊匹配的材料。 这将极大地增加你技术文件翻译的报价。 MindReader简介 这篇博客并不是来推销的,所以这个部分我长话短说。 人们即使规划地非常好,在写作时也很难保持一致。一致性问题很可能只在校对阶段才会被发现,或者可能会被完全忽视。 为此,STAR Group开发了帮助写作的工具:MindReader以及MindReader for Outlook。 就像该公司的任何其他工具一样,MinderReader的原则是只处理新的内容。 把它想像成手机上的自动完成。 只需开始键入你的句子,该工具就会提供来自你文档中其他地方的建议。 如果你想重用它们,你可以确认重用。 如果你不想,你可以忽略。 这个工具可以帮助你在进行技术写作时保持一致,使行文更加清晰,并降低潜在的翻译成本。 如果你对这个工具感兴趣,现在就可以联系我们的团队。 我希望这篇博客能为你提供一些建议,帮助你提高技术写作水平,降低翻译成本。 如果你想了解更多关于这方面的信息,或者想和团队的成员讨论一个潜在的项目,请即刻联系我们。

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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