The Nimdzi Language Technology Atlas 2019 is out. The number of individual products mapped has increased from over 400 to over 500. Let’s have a look at what’s new and what has changed in language technology over the last year.
2019年Nimdzi 语言技术谱系图发布。列入的单个语言技术产品数量从400多个增加到500多个。让我们来看看在过去的一年里语言技术有什么新的发展和变化。
Machine translation made customizable
可定制的机器翻译
With the introduction of AutoML in July 2018, Google Translate became trainable. Microsoft upgraded its Custom Translator as well. This removed a key differentiator of the dedicated machine translation (MT) vendors, such as Kantan MT, Omniscien, and Systran. Now, popular MT is trainable, and customization is almost as easy as uploading a translation memory into the cloud. Therefore the previous classification of “Generic” and “Trainable” MT became obsolete. We retired it in favor of a softer distinction, depending on how you approach the piece of MT technology – “ready-to-use” or the “build” approach. We also removed the MT toolkits category. MT has progressed too far to build it from scratch anymore.
随着 AutoML 在2018年7月的引入，谷歌翻译变得可训练了。微软也升级了它的定制翻译器。这消除了专用机器翻译(MT)供应商的一个关键差异，比如 kantanmt、 Omniscien 和 Systran。现在，流行的机器翻译是可学习的，而且定制几乎和上传翻译内存到云中一样简单。因此，以前对'通用'和'可训练'机器翻译的分类已经过时。取决于您如何处理 MT 技术——我们取消了'随时可用'或'构建'的分法，以支持更灵活的区分。我们还删除了 MT 工具包类别。机器翻译已经取得了很大的进展，不可能再从头开始构建了。
Next, MT is to add predictive quality analytics and make them stick with the professional community. Moreover, a few key MT vendors still need to add customization ability to their engines, chief among them being DeepL and Yandex Translate.
接下来，MT 将增加预测性质量分析，并让它们与专业社区保持一致。此外，一些主要的机器翻译厂商仍然需要为他们的引擎添加定制功能，其中最主要的是 DeepL 和 Yandex Translate。
LSPs rolled out terminology tools
语言服务提供商推出了术语工具
New terminology solutions have hit the market in 2018-2019, developed by language service providers (LSPs) such as Semantix, Bureau Works, EGO Translating, Institut für technische Literatur, and Toptranslation in Germany. The new terminology tools typically come as a supplement to a translation management system. They add a workflow for the terminologist to approve terms, the ability to link terms with images, group terms into clouds or “trees,” and a log-in integration with Active Directory that allows all Windows users within an organization to access terminology without buying additional TMS licenses. The next goal for terminology tech is to integrate with more content creating and authoring tools in documentation, marketing, and support departments.
新的术语解决方案在2018-2019年上市，由语言服务提供商(LSPs)开发，如 Semantix，Bureau Works，EGO Translating，Institut f r technische Literatur，and Toptranslation in Germany。新的术语工具通常是对翻译管理系统的补充。他们增加了一个工作流程，让术语专家批准术语，能够将术语与图像联系起来，将术语分组到云或'树'中，以及与 Active Directory 的登录集成，允许组织内的所有 Windows 用户访问术语，而无需购买额外的翻译管理系统许可证。术语技术的下一个目标是与文档、市场和支持部门中的更多内容创建和编写工具集成。
New media localization tools
新的媒体本地化工具
New media localization tools and functionalities are a response to the content boom of TV series and films. TransPerfect launched Media.Next suite, Omniscien announced Media Studio, dotsub rolled out workflow management in videotms.com, while CAT tools such as memoQ, SDL Trados, and Wordbee added video players to facilitate subtitling. At the same time, established media localization systems such as Zoo Dubs and Subs, Subtitle NXT and Ooona continued to grow and gain traction.
新媒体本土化工具和功能是对电视剧和电影内容热潮的回应。TransPerfect 发布了Media.Next suite。Omniscien发布了Media Studio，Dotsub 在videotms.com网站上推出了工作流管理，而 CAT 工具，如 memeQ，SDL Trados，和 Wordbee 增加了视频播放器以方便字幕翻译。与此同时，已建立的媒体本地化系统，如 Zoo Dubs and Subs，Subtitle NXT 和 Ooona 继续增长和获得关注。
New technology in media localization is steering towards text-to-speech and machine translation technology to shift to human post-editing environments. It’s also catching up with translation management systems on workflow management, collaboration, and integration functionalities.
媒体本地化的新技术正朝着文本到语音方向发展，机器翻译则转向译员的后编辑环境。它还在工作流管理、协作和集成功能方面紧跟翻译管理系统。
Interpreting tech focused on automation
口译技术专注于自动化
New launches in remote interpreting petered out once the market has reached platform abundance. It became apparent that remote interpreting is going to be more difficult than taking video chat technology from Google WebRTC and adding a custom interface with a logo to it.
一旦市场达到平台丰富，新推出的远程口译就会逐渐消失。显而易见，远程翻译将比从 Google WebRTC 获得视频聊天技术并添加一个带有 logo 的自定义界面更加困难。
Instead, new development centered on management functionalities, to help small and medium-sized LSPs automate the hundreds and thousands of bookings they manage every month, primarily those in the United States. Companies like Lango, uSked, and Total Language make their appearance on our map for the first time this year, as they make their presence in the market known.
相反，新的发展集中在管理功能上，帮助中小型的语言服务提供商自动化他们每月管理的成百上千的订单，主要需求在美国。像 Lango，uSked，和 Total Language 这样的公司今年第一次出现在我们的图谱上，因为他们在市场上的存在是众所周知的。
In the future, it is likely that technology developers will make remote interpreting call centers easy to build and accessible to small companies around the world while integrating all of them into networks via APIs. Interpreting has yet to make heavy use of AI, speech recognition, and machine translation.
未来，技术开发人员可能会使远程翻译呼叫中心易于建立，并且可以被世界各地的小公司访问，同时通过 api 将所有这些中心集成到网络中。口译还需要大量使用人工智能、语音识别和机器翻译。
Data services production platforms emerge
数据服务生产平台涌现
To highlight the fact that the top LSPs already make millions in data services and AI training, and have started to automate these service lines, we’ve added the Machine Intelligence category to the landscape. “Production platforms” comprise software that allows miscellaneous tasks such as tagging, annotation, data labeling, and voice transcription to run at scale with hundreds of data workers taking part. So far, Nimdzi has learned about only a handful of products in this class. All of them are proprietary products developed by large LSPs. So far, we haven’t identified a commercially available technology in this niche. “Natural language understanding” includes machine learning systems with APIs that LSPs use to build chatbots.
为了强调这样一个事实，即顶级的语言服务提供商已经在数据服务和人工智能培训方面赚取了数百万美元，并且已经开始实现这些服务线的自动化，我们已经将机器智能类别添加到了该图谱中。'生产平台'包括一些软件，这些软件允许各种各样的任务，比如打标、注释、数据标记和语音转录，在数百名数据工作者的参与下大规模运行。到目前为止，Nimdzi 在这个类别只研究了少量产品。都是由大型语言服务提供商开发的专有产品。到目前为止，我们还没有在这个利基市场上找到一种商业上可行的技术。'自然语言理解'包括带有 api 的机器学习系统，语言服务提供商使用这些 api 构建聊天机器人。
Language AI is a field that comprises hundreds of technologies and startups. However, LSPs only use a small portion of them to deliver language services, which we wanted to reflect upon in this year’s Technology Atlas.
语言人工智能是一个由数百种技术和创业公司组成的领域。然而，语言服务提供商只使用其中的一小部分来提供语言服务，我们希望反应到今年的技术图谱中。
Still under a billion in language tech
语言技术还不到10亿美元
Technology vendors continue to pop up and grow, but the whole language services tech field remains relatively small in terms of total revenue. Our estimate for the market size is just shy of USD 800 million, or about 1.6% of the services market.
技术供应商继续涌现和成长，但整个语言服务技术领域的总收入仍然相对较小。我们对市场规模的估计略低于8亿美元，约占服务市场的1.6% 。
There are no technology unicorns in the language services industry. In business core functions such as dev ops, marketing, and support, the likes of Atlassian, Hubspot or Zendesk continue to rake in billions. But no one has been able to do so with language tools — at least not yet.
语言服务行业没有技术独角兽。在商业核心功能如开发、营销和支持方面，像 Atlassian、 Hubspot 和 Zendesk 这样的公司继续赚取数十亿美元。但是至今还没有人能够用语言工具做到这一点，至少现在还没有。
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英文原文来源：Nimdzi官网，版权归Nimdzi所有。
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