留给人类译者的时间不多了

Photo by Joonas Sild on Unsplash

《经济学人》前些日子发了篇文章，专门探讨了机翻的发展。似乎，机翻已是大势所趋，人类翻译即将全面下岗。

学翻译的、做翻译的、用翻译的，肯定对机翻都不陌生。谈到机翻，业内既有“明天下岗”的声音，也有“避而不谈”的沉默。

实际上，在GenAI的加持下，不少机翻工具的确取得了长足的进步。

会不会取代人类？

会，但没大家想的那么快。

讨论机翻，必然还是要确定下语对，比方说英法互译，机翻的准确率已经相当高了。中英互译，机翻只能说勉勉强强，甚至达不到差强人意的水平。

当然，我不是拿什么什么理论在和你们空说。最近几个月，我都有尝试各类机翻软件，包括DeepL、有道、Google、ChatGPT等等。至少从我手头的文本来看，三五年内是不大有希望取代人类。

但是，也有一个现象就是MTPE（机翻后人工审校）的场景越来越多了。以后我们可以再来聊聊这个问题，今天先节选几段TE文章，感受下趋势：

Vasco Pedro had always believed that, despite the rise of artificial intelligence (AI), getting machines to translate languages as well as professional translators do would always need a human in the loop. Then he saw the results of a competition run by his Lisbon-based startup, Unbabel, pitting its latest AI model against the company’s human translators. “I was like…no, we’re done,” he says. “Humans are done in translation.” Mr Pedro estimates that human labour currently accounts for around 95% of the global translation industry. In the next three years, he reckons, human involvement will drop to near zero

以翻译技术公司老板的视角对比两个观点。老观点：机翻不错，但也离不开专业译者的参与；新观点：人工翻译要亡了

In the loop：If someone is in the loop, they are part of a group of people who make decisions about important things, or they know about these decisions；这个表达在邮件里面很常见

Pit A against B：to test sb or their strength, intelligence, etc. in a struggle or contest against sb/sth else；看个例句，The physical store strategy is expensive and will pit Amazon against far more experienced players, such as Walmart

Photo by Katharina Roehler on Unsplash

In Unbabel’s test, human and machine translators were asked to translate everything from casual text messages to dense legal contracts and the archaic English of an old translation of “Meditations” by Marcus Aurelius. Unbabel’s AI model held its own. Measured by Multidimensional Quality Metrics, a framework that tracks translation quality, humans were better than machines if they were fluent in both languages and also experts in the material being translated (for instance, specialist legal translators dealing with contracts). But the lead was small, says Mr Pedro, who added that it would be hard to see how, two or three years from now, machines would not overtake humans entirely

承接上文的competition run by his start-up，介绍了人工翻译vs机器翻译的比赛内容，包括法律合同、《沉思录》等等

结论是专业的译者更厉害，但是厉害得不多（the lead was small）。最后一句给出预测，还剩下2-3年的时间

Marco Trombetti, boss of Translated, based in Rome, has created a different measure for the quality of machine translations, called Time to Edit (TTE). This is the amount of time it takes a human translator to check a transcript produced by a machine. The more errors in the transcript, the slower the human has to go. Between 2017 and 2022 TTE dropped from three seconds per word to two across the ten most-translated languages. Mr Trombetti predicts it will fall to one second in the next two years. At that point, a human is adding little to the process for most tasks other than what Madeleine Clare Elish, head of responsible AI at Google Cloud, calls a “moral crumple zone”: a face to take the blame when things go wrong, but with no reasonable expectation of improving outcomes

又有个老板搞了个机翻质量测试标准TTE。简单说，就是以审校速度衡量机翻的水平（从我实际体验来看，审校机翻的速度不比我重新翻译快多少）。

目前水平是一字两秒，以后可能会是一字一秒。届时，人类译员就变成了机翻的“背锅侠”（这谁看了不得比个6）

Photo by Willian Justen de Vasconcellos on Unsplash

The problem of translating one sentence to another is “pretty close to solved” for those “high-resource” languages with the most training data, says Isaac Caswell, a research scientist at Google Translate. But going beyond this to make machine translation as good as a multilingual person—especially for languages that do not have reams of available training data—will be a more daunting task

机器已经基本搞定了句子层面的翻译，但还得再练练。High-resource language可以简单理解为语料比较多的语言（中文虽然语料很多，但是质量普遍很差，这也是导致中英翻译AI做的没有想象中好的原因）

Complex translations face the same problems that plague LLMs in general. Without the ability to plan, refer to long-term memory, draw from factual sources or revise their output, even the best translation tools struggle with book-length work, or precision tasks such as keeping a translated headline to a certain length. Even tasks that a human finds trivial still trip them up. They will, for instance, “forget” translations for static phrases like shop names, translating them afresh, and often differently, each time they are encountered. They may also hallucinate information they don’t have to fit grammatical structures of the target language. “To have the perfect translation, you also have to have human-level intelligence,” says Mr Caswell. Without being a competent poet, it is difficult to translate a haiku

本段主要列举了一些机翻的常见问题。1）缺少翻译项目规划的能力；2）缺少长期记忆提取的能力；3）缺少事实信息来源提炼的能力；4）缺少完善译文的能力

并且举了2个例子：固定词组翻完就忘的问题，说白了就是文内用词不统一；硬凑语法，瞎编信息

That is if users can even agree on what a perfect translation is. Translation has long been a struggle between “transparency” and “fidelity”—the choice between translating sentences exactly as they are in the original language, or exactly as they feel to the target audience. A transparent translation would leave an idiomatic phrase as it is, letting English speakers hear a Pole dismiss a problem as “not my circus, not my monkeys”; a faithful one may even go so far as to change whole cultural references, so that Americans aren’t taken off-guard by “football-shaped” being used to describe a spherical object

这里谈了谈“信”的问题，是追求字面的“信”，还追求具备读者意识的“信”

Even if there could be a simple dial to turn between transparency and fidelity, perfecting the interface of such a system would require AI assistance. Translating between languages can sometimes require more information than is present in the source material: to translate “I like you” from English to Japanese, for instance, a person needs to know the gender of the speaker, their relationship to the person they are addressing and ideally their name to avoid the impolite use of the word “you”. A perfect machine translator would need to be able to interpret and replicate all these subtle cues and inflections

假如可以有个指针，左边是“直译”，右边是“意译”，用户可以根据刻度自己选择，那就万事大吉了吗？

非也，非也，靠谱的译者还要考虑文本外的信息（Translating between languages can sometimes require more information than is present in the source material）

Photo by Moritz Kindler on Unsplash

Then there is the issue of “low-resource” languages, where the paucity of written text means that the accuracy of translations is not being improved by the LLM breakthroughs that have transformed the rest of the industry. Less data-hungry approaches are being tested. A team at Google, for instance, built a system to add speech-to-speech translation for 15 African languages. Rather than being trained on gigabytes of audio data, it instead learns to read written words the same way a child would, associating speech sounds with sequences of characters in written form

语料少也是个问题，但谷歌等科技公司也在想办法突破。