What other creative professions can learn from translation about the effects...
关于翻译的效果，其他创造性职业可以从翻译中学到什么...
On Nov. 30, OpenAI publicly launched the ChatGPT chatbot built on its GPT-3 large language model (LLM). The new chatbot quickly caught the world’s attention, garnering over 1 million users in the first five days after its release. Its ability to provide plausible, well-written answers in many different languages and writing styles in response to natural language queries made in different languages astounded the general public and set off alarm bells in education and many of the creative professions, like copywriting, advertising, and journalism. Several of the largest school districts in the United States quickly banned ChatGPT outright from their networks, citing concerns over academic honesty. Even a leading artificial intelligence conference banned the use of ChatGPT to help write papers for submission.
11月30日，OpenAI公开推出了基于GPT-3大型语言模型（LLM）的ChatGPT聊天机器人。新的聊天机器人很快引起了世界的关注，在发布后的头五天内就吸引了超过100万用户。它能够用多种不同的语言和写作风格提供可信的、写得很好的答案，以回应用不同语言提出的自然语言问题，这一能力震惊了广大公众，并在教育和许多创意行业（如文案、广告和新闻业）中敲响了警钟。美国几个最大的学区很快就禁止了ChatGPT在其网络中的使用，理由是担心学术诚信。甚至一个领先的人工智能会议也禁止使用ChatGPT来帮助撰写提交的论文。
People in creative professions like advertising, technical writing, journalism, and illustration began imagining doomsday scenarios of being replaced by technology, with generative AI tools like chatbots taking over their work. To translators, this all sounds remarkably familiar.
广告、技术写作、新闻和插图等创意职业的人们开始想象被技术取代的世界末日场景，聊天机器人等生成性人工智能工具将接管他们的工作。对于译者来说，这一切听起来非常熟悉。
The translation profession has not been known traditionally for being ahead of the technological curve, but in this case, it is by about six or seven years. The concerns currently running through academe and the creative professions are strikingly similar to those expressed by translators beginning in 2016 with the release of Google’s neural machine translation (NMT) service. These concerns only intensified when another NMT service, DeepL, was released in 2017. As a result, translation has had a head start in adapting and managing the technological change brought about by state-of-the-art artificial intelligence, specifically NMT.
传统上，翻译行业并不以领先于技术曲线而闻名，但在这种情况下，它的领先时间大约为六到七年。目前学术界和创意界的担忧与2016年谷歌神经机器翻译（NMT）服务发布后译者表达的担忧惊人地相似。当另一项NMT服务DeepL于2017年发布时，这些担忧才加剧。因此，翻译在适应和管理最先进的人工智能（特别是NMT）带来的技术变革方面取得了领先地位。
NMT technologies like deep neural networks and LLMs and the machine translation tools and chatbots built on them treat data in similar ways. “Translation is the key to everything else,” said German-English translator and translation technology expert Jost Zetzsche. “Once translation is done well by machines, everything else falls into place. This is how developers saw it, and it is why they focused on translation first.”
深度神经网络和LLM等NMT技术以及基于它们的机器翻译工具和聊天机器人以类似的方式处理数据。“翻译是其他一切的关键，”德语-英语翻译家兼翻译技术专家Jost Zetzsche说。“一旦机器完成了翻译，其他一切都会到位。这就是开发者的看法，也是他们首先关注翻译的原因。”
This same technology that enabled NMT to improve machine translation is now finding its way into seemingly unrelated products (think ChatGPT or Dall·E). The translation “problem” has not been solved with the latest advancements in NMT, but there are lessons to be learned in other professions from how the professional translation community has been affected by and adapted to the widespread use of NMT both by translators and by end users.
这项使NMT能够改进机器翻译的技术，现在正被引入看似无关的产品中（想想ChatGPT或Dall·E）。NMT的最新进展并没有解决翻译“问题”，但从专业翻译界如何受到译者和最终用户广泛使用NMT的影响和适应中，其他行业也可以吸取教训。
Five Lessons Learned from Translation Since the Launch of NMT
自NMT推出以来从翻译中吸取的五个教训
When a potentially disruptive technology is released, marketers differentiate the product from any competition and focus on wowing potential clients and the general public. Media attention depends greatly on that wow factor to create a big marketing splash and boost awareness. As a result, press releases and initial media coverage tend to overflow with superlatives and focus on any example that suggests the day of artificial general intelligence has arrived. Just think about all those Star Trek Universal Translator and Babelfish headlines you’ve seen over the years. However, the real impact of a new technology on actual workloads and workflows only becomes apparent over time.
当一项具有潜在破坏性的技术发布时，营销人员会将产品与任何竞争对手区分开来，并专注于吸引潜在客户和公众。媒体的关注在很大程度上取决于这种令人惊叹的因素，以创造巨大的营销轰动效应并提高知名度。因此，新闻稿和最初的媒体报道往往充斥着最高级的词汇，并关注任何表明人工智能时代已经到来的例子。想想这些年来你看到的《星际迷航通用翻译机》和《巴别鱼》的头条新闻。然而，新技术对实际工作负载和工作流的真正影响只会随着时间的推移而变得明显。
Here are five lessons that emerged from interviews conducted with practicing translators about how NMT has changed the way they work over the past six years.
以下是从对执业翻译的采访中得出的五个教训，关于NMT在过去六年中如何改变了他们的工作方式。
Translators Are Still Very Much in Demand
翻译人员的需求量仍然很大
That’s the headline. Translators are still in high demand. But that doesn’t mean that they haven’t had to adapt. NMT tools like DeepL, Google Translate, and Microsoft Translate, are just that: tools. And like other tools, a hammer for instance, they can be used by anyone from skilled professionals to do-it-yourselfers with varying levels of skill and success. According to Dion Wiggins, CTO and co-founder of Omniscien Technologies, consumer NMT services have become quite mature with consumer-level use cases now well established. Google alone translates more than 100 billion words a day.
这是头条新闻。翻译人员的需求仍然很高。但这并不意味着他们不需要适应。像DeepL、Google Translate和Microsoft Translate这样的NMT工具就是：工具。和其他工具一样，例如锤子，任何人都可以使用它们，从熟练的专业人员到不同水平的技能和成功的自己动手。根据OmniscienceTechnologies的首席技术官兼联合创始人DionWiggins的说法，消费者NMT服务已经相当成熟，消费者级用例已经建立。仅谷歌一家每天就翻译超过1000亿个单词。
That said, it is important to note that these use cases generally cover translation tasks that were never originally handled by professional translators in the first place. With this broad use of the technology, end users have become more aware of NMT’s abilities and limitations. In the end, NMT has made cross-language communication more available than ever, and although there are inevitable misapplications of the technology, consumers are increasingly savvy about the limitations to what NMT can do and when they will need a professional translator.
也就是说，需要注意的是，这些用例通常涵盖最初从未由专业翻译人员处理过的翻译任务。随着该技术的广泛应用，最终用户已经更加意识到NMT的能力和局限性。最终，NMT使跨语言交流变得比以往任何时候都更容易使用，尽管这项技术不可避免地被误用，但消费者越来越了解NMT能做什么以及何时需要专业翻译的局限性。
As literary translator Tim Gutteridge put it, “Translation is not a cake of a finite size. [Technology] will allow people to translate things. But it’s not a zero-sum game.” However, just where the line dividing when NMT is good enough and when a human translator is preferrable is dynamic and will continue to change and encroach upon certain translation market segments.
正如文学翻译家蒂姆·古特里奇（Tim Gutteridge）所言，“翻译不是一块有限大小的蛋糕。（技术）将允许人们翻译东西。但这不是一场零和游戏。”然而，当NMT足够好时，以及当人类翻译者更适合时，界线的划分是动态的，并将继续改变并蚕食某些翻译市场细分市场。
Translators Have Learned that NMT Can Increase Their Productivity
译者已经认识到NMT可以提高他们的工作效率
All translators interviewed for this article said that the use of NMT tools has had a positive effect on their productivity, directly or indirectly. Many noted significant increases in their productivity because of how they employ NMT tools.
本文采访的所有翻译人员都表示，NMT工具的使用直接或间接地对他们的生产力产生了积极的影响。许多人注意到，由于他们使用NMT工具的方式，他们的生产率显著提高。
The main time saver mentioned was using NMT just to get the target text quickly into the computer and on screen without having to type tens of thousands of words on a keyboard. Once in electronic form, the draft target text can be adapted and corrected using the translator’s preferred tool. Granted, this approach works better with certain types of texts that lend themselves to machine translation, like news articles or instruction manuals.
所提到的主要节省时间的方法是使用NMT将目标文本快速输入计算机和屏幕，而无需在键盘上键入数万个单词。一旦成为电子形式，目标文本草案就可以使用译者的首选工具进行调整和更正。当然，这种方法在某些适合机器翻译的文本类型（如新闻文章或说明书）中更有效。
“DeepL was a game changer for translating press articles. I have easily doubled my output and shortened turnaround times. It streamlines my work. I have to do some post editing, based on what country the article is from. But it’s a tool, not a crutch,” said Donatella Ungredda, a US-based Spanish-English translator and interpreter.
“DeepL是翻译新闻文章的游戏规则改变者。我轻松地将产量翻了一番，缩短了周转时间。它简化了我的工作。我必须做一些后期编辑，根据文章来自哪个国家。但它是一种工具，而不是拐杖，”美国的西班牙语-英语笔译员Donatella Ungredda说。
However, translators had to learn to recognize the quirks of NMT and the texts it does not handle well. Thomas West, a US-based translator specializing exclusively in legal translation, noted, “I ran an agency for 25 years. I edited a lot of human translation. I’m comfortable editing. But it has taken me time to get used to the mistakes that DeepL makes because they are different. Consistency is very important in English legal writing.”
然而，译者必须学会识别NMT的怪癖和它处理不好的文本。专门从事法律翻译的美国翻译家托马斯·韦斯特（Thomas West）指出，“我在一家机构工作了25年。我编辑了很多人工翻译。我很喜欢编辑。但我花了很多时间来适应DeepL所犯的错误，因为它们不同。一致性在英语法律写作中非常重要。”
“If a writer uses a synonym, it must be for a specific reason,” West continued. “DeepL is a smorgasbord of switching synonyms, which can be a mess for legal translation. DeepL does not do well with Latin American legal documents either specifically because of the terminological variation in the source text.”
“如果一个作家使用同义词，那一定是出于特定的原因，”韦斯特继续说道。“DeepL是一个转换同义词的大杂烩，这对法律翻译来说可能是一团糟。DeepL对拉丁美洲的法律文件也做得不好，特别是因为源文本中的术语差异。”
Some Types of Translation and Market Segments Are Being Dominated by NMT
一些类型的翻译和细分市场正被NMT所主导
For decades, the translation profession has operated on the premise that every translation must be 100-percent accurate and of impeccable linguistic quality. With the advent of deep neural networks, the easy-to-spot awkward syntax and grammar produced by previous translation models have largely been replaced with other more subtle problems like hallucinations, deletions, and mistranslations. Even so, it is now becoming clear that more and more end users of translation have concluded that NMT can be good enough for certain types of texts and use cases. Informational texts that have a limited useful life, like news reports and different types of legal documents, such as court decisions, are now often translated using NMT services and are seldom post edited. Some end users are prioritizing turnaround times and costs over fluency and even translation accuracy.
几十年来，翻译行业一直以每一个翻译都必须100%准确、语言质量无可挑剔为前提。随着深度神经网络的出现，以前的翻译模型所产生的难以识别的语法和语法已经被其他更微妙的问题所取代，如幻觉、删除和误译。尽管如此，现在越来越多的翻译最终用户已经得出结论，NMT对于某些类型的文本和用例来说已经足够好了。有用期有限的信息文本，如新闻报道和不同类型的法律文件，如法院判决，现在通常使用NMT服务进行翻译，很少进行后期编辑。一些最终用户将周转时间和成本放在流畅度甚至翻译准确性之上。
The shift to post-edited machine translation (PEMT) by large agencies and some direct clients is affecting bottom lines. For translators who have seen their work move to all post editing, their income has taken a hit, according to Zetzsche, as the size of contracts for post-editing work tend to be significantly smaller than for traditional translation work. As this trend has grown, some translators have transitioned to other language services or left the profession altogether, while others still have chosen to focus on working with direct clients in the high-level or premium market, which so far has proven more skeptical of the NMT trend.
大型机构和一些直接客户转向后编辑机器翻译（PEMT）正在影响底线。Zetzsche表示，对于那些看到自己的工作转向所有后期编辑的译者来说，他们的收入受到了打击，因为后期编辑工作的合同规模往往远小于传统翻译工作。随着这一趋势的发展，一些翻译人员已经转向其他语言服务或完全离开了这一行业，而另一些翻译人员仍然选择专注于与高级或高级市场的直接客户合作，迄今为止，这已被证明对NMT趋势更持怀疑态度。
Another market segment that is evolving with the advent of NMT is legal translation. West explains that “good-enough” translation is gaining a foothold in the legal sector. “But you must really narrow down and focus on what the translation will be used for,” he said. “Legal translation is largely for informational purposes. The translation must be accurate, but it may have never needed to be word smithed because it is for informational purposes only. These are not texts that are going into a glossy brochure.”
随着NMT的出现，另一个正在发展的细分市场是法律翻译。韦斯特解释说，“足够好”的翻译正在法律界站稳脚跟。“但你必须真正缩小范围，专注于翻译的用途，”他说。“法律翻译主要是为了提供信息。翻译必须准确，但可能从来都不需要逐字逐句，因为它只是为了提供信息而已。这些文本不会被载入精美的小册子。”
Translators Have Been Creative and Cautious in How They Have Used NMT Tools
译者在如何使用NMT工具方面具有创造性和谨慎性
Frequently, translators assume that NMT will lead to them doing nothing but post editing. The past six years have shown otherwise. “Some colleagues are going to all post editing. I’ve tended to go in the opposite direction,” says Tim Gutteridge, a UK-based Spanish-to-English translator. For those that have seen their work transition to all post editing, their income has taken a hit, particularly translators who work almost exclusively for large translation companies, according to Zetzsche.
通常，译者认为NMT会导致他们只做后期编辑。过去六年的情况并非如此。英国西班牙语到英语翻译Tim Gutteridge表示：“有些同事会去做后期编辑。我倾向于相反的方向。”。根据Zetzsche的说法，对于那些已经从工作过渡到所有后期编辑的人来说，他们的收入受到了打击，尤其是那些几乎只为大型翻译公司工作的译者。
Today, translators use NMT tools, such as Google Translate and DeepL, as suggestion tools while translating. Zetzsche explained, “Most translators use NMT much as they use other tools like translation memories, term bases, or corpora.”
如今，翻译人员在翻译时使用NMT工具，如Google Translate和DeepL，作为建议工具。Zetzsche解释说：“大多数译者使用NMT就像他们使用其他工具一样，如翻译记忆库、术语库或语料库。”
US-based English-and-Spanish-to-Italian translator Riccardo Schiaffino agrees. “I use the suggestions probably more than 50% of the time,” he said. “You can see if [the tool] will be useful at the beginning of a project. Interactive mode speeds up my work a little bit. But it doesn’t improve the quality of the work. It’s like having a really good translation memory.”
美国的英语和西班牙语到意大利语的翻译Riccardo Schiaffino同意这一观点。“我可能有超过50%的时间使用这些建议，”他说。“你可以在项目开始的时候看看[这个工具]是否有用。互动模式稍微加快了我的工作速度。但并没有提高作品的质量。这就像有一个非常好的翻译记忆库。”
“I use DeepL to find other options. It helps you determine when a translation is off,” said Hedwig Spitzer, a French to Spanish translator and conference interpreter based in Peru.
“我使用DeepL来寻找其他选项。它帮助你确定翻译何时关闭，”秘鲁的法语到西班牙语翻译和会议口译员海德薇·斯皮策说。
Spitzer also shared another novel application of NMT tools to assist with conference interpreting. “I use DeepL when I receive speeches at the last minute. Because speakers often read dense speeches at the speed of light, we usually do a quick machine translation of the speech if it is a public event. But it still must be reviewed.”
Spitzer还分享了NMT工具的另一个新应用，以帮助会议口译。“当我在最后一分钟收到演讲时，我会使用DeepL。因为演讲者经常以光速阅读密集的演讲，如果是公共活动，我们通常会对演讲进行快速机器翻译。但仍需重新审视。”
This is a growing practice in conference interpreting as more and more events are heavily scripted and recorded for on-demand viewing in multiple languages, further blurring the lines between translation and interpreting.
这种做法在会议口译中越来越普遍，因为越来越多的活动需要大量脚本和录制，以便以多种语言点播观看，这进一步模糊了笔译和口译之间的界限。
Interestingly, even when using the paid versions of NMT tools, many translators are still skeptical about data security claims, and many refuse to employ the technology for certain clients with particularly sensitive topics or when the translated content is of a confidential nature. Some clients even include clauses in their contracts prohibiting the use of machine translation. This concern, however, may be overstated.
有趣的是，即使在使用NMT工具的付费版本时，许多翻译人员仍然对数据安全声明持怀疑态度，许多人拒绝为某些具有特别敏感主题或翻译内容具有机密性质的客户使用该技术。一些客户甚至在合同中加入禁止使用机器翻译的条款。然而，这种担忧可能被夸大了。
“The privacy problem is kind of solved for most engines,” Zetzsche said. “As long as you’re using the API, Google, Microsoft, and DeepL are guaranteeing not to use the data. The problem, in my opinion, is that translators have used that privacy argument against MT so much that they either can’t or don’t want to realize this.”
“对于大多数引擎来说，隐私问题已经解决了，”Zetzsche说。“只要你在使用API，Google、Microsoft和DeepL保证不使用这些数据。在我看来，问题在于译者太多地利用隐私来反对机器翻译，以至于他们要么不能，要么不想意识到这一点。”
One thing is certain. Data privacy and the copyright of the data used to train LLMs are two significant controversies yet to be resolved when it comes to generative AI.
有一点是肯定的。当谈到生成性人工智能时，数据隐私和用于训练LLM的数据的版权是两个有待解决的重大争议。
The Job of Translators will Continue to Evolve
笔译员的工作将继续发展
The use of NMT in business scenarios is still evolving, with applications of the technology still in development. Professional translators have had to adapt and will need to continue to adapt as NMT is applied in more business scenarios.
NMT在业务场景中的使用仍在发展，该技术的应用仍在开发中。随着NMT在更多商业场景中的应用，专业翻译不得不适应并将需要继续适应。
Practicing translators have eventually adopted new technologies into workflows, while at the same time emphasizing the more human aspects of translation, like cultural knowledge, domain expertise, and having a feel for what sounds right, as essential to producing professional-quality translations. Ceding ground to technology in professional practice is still a sensitive subject.
实践中的译者最终在工作流程中采用了新技术，同时强调翻译更人性化的方面，如文化知识、领域专业知识，以及对听起来正确的感觉，这是制作专业质量翻译的关键。在专业实践中向技术让步仍然是一个敏感话题。
Case in point, PEMT. The majority of translators interviewed for this article, for example, have begun offering PEMT as one of their services, albeit reluctantly. Schiaffino began using MT for his work when DeepL was introduced in 2017. As more of his customers move toward PEMT, he decided to offer the whole machine translation and post editing process as a service, as a way to improve quality control.
PEMT就是一个很好的例子。例如，本文采访的大多数译者已经开始提供PEMT作为他们的服务之一，尽管他们并不情愿。2017年推出DeepL时，Schiafaro开始使用MT进行工作。随着越来越多的客户转向PEMT，他决定将整个机器翻译和后期编辑过程作为一项服务，以提高质量控制。
“I don’t like doing post editing very much but will do it for certain clients,” he said.
“我不太喜欢做后期编辑，但会为某些客户做，”他说。
Other translators interviewed for this article noted that they use machine translation in combination with post editing on a regular basis but asked to remain anonymous.
本文采访的其他翻译指出，他们经常将机器翻译与帖子编辑结合使用，但要求匿名。
“The very top of the market is not going to be touched at all by this. The rest in one way or another will be touched. If we are smart, we are going to use engines like DeepL ourselves,” said Schiaffino.
“市场的最顶端根本不会受到影响。其余的将以这样或那样的方式被触及。如果我们聪明的话，我们会自己使用像DeepL这样的引擎，”Schiaffino说。
One thing is sure, the role of the professional translator has evolved over the last six years and will continue to evolve at ever-increasing speed.
有一点是肯定的，专业翻译的角色在过去的六年里已经发生了变化，并将继续以越来越快的速度发展。
What’s on the Horizon?
地平线上有什么？
The accelerated development of generative AI tools, like LLMs and AI assistants, is poised to have a significant effect on the professional practice of translation and may radically change the notion of what professional translation is. It is this set of emerging technologies that may prove to be the dark horse in this race of innovation and disruption. And the language services industry would do well to keep a close eye on how and where it is running.
生成型人工智能工具（如LLM和人工智能助手）的加速发展将对翻译的专业实践产生重大影响，并可能从根本上改变专业翻译的概念。正是这一系列新兴技术可能成为这场创新和颠覆竞赛中的黑马。语言服务行业最好密切关注其运行方式和位置。
During a recent online discussion on the future of machine translation, Alon Lavie, VP of language technologies at Unbabel and consulting professor at Carnegie Mellon University, predicted that in a few years’ time, much content will not need to be pre-generated. Clients will rely on AI agents to tell them what they want to know in real time. According to Lavie, there may not be a need to pre-generate content that would then need to be translated. In the future, content will be generated on the fly in multiple languages.
在最近一次关于机器翻译未来的在线讨论中，Unabel语言技术副总裁、卡耐基梅隆大学咨询教授阿隆·拉维（Alon Lavie）预测，几年后，许多内容将不需要预先生成。客户将依靠人工智能代理实时告诉他们他们想知道什么。根据拉维的说法，可能不需要预先生成需要翻译的内容。未来，内容将以多种语言实时生成。
What will this mean for current translation workflows? How will this and other technological developments affect the definition of what it means to be a translator?
这对当前的翻译工作流程意味着什么？这一技术发展和其他技术发展将如何影响翻译的定义？
Jay Marciano, director of MT outreach and strategy at Lengoo, puts it this way: “As errors become much more related to the meaning than to grammatical problems, subject area expertise almost becomes more important than language skills. If you have an MA in translation, you need to have more subject area expertise. Your work will change, but it will not go away.”
Lengoo的机器翻译拓展和战略主任杰伊·马西亚诺这样说：“随着错误变得更多地与意义而不是语法问题有关，学科领域的专业知识几乎变得比语言技能更重要。如果你有翻译硕士学位，你需要有更多的学科领域的专业知识。你的工作会改变，但不会消失。”
