When a language you don’t understand appears in your Facebook News Feed, you can touch a button and quickly translate it. Facebook offers a way of communicating not just with the millions of people who speak your language, but with millions of others who speak something else. Or at least it almost does.

当你的脸书动态消息中出现了一种你不懂的语言，你可以点击一个按钮迅速翻译这些内容。脸书提供了一种方法使你不仅可以与数百万会讲你的语言的人交流，还可以与数百万讲其他语言的人交流。或者至少可以说脸书几乎做到了。

This morning, the company’s central artificial-intelligence lab released a paper describing a new technology that could accelerate the evolution of machine translation not only inside Facebook but across the internet. According to Facebook’s tests, its technique does so far more efficiently¹ than other methods, which could eventually lead to even sharper translations.

今天上午脸书公司人工智能中心实验室发表了一篇论文，介绍了一种可以加速机器翻译发展的新技术，这种技术不仅可以加速脸书公司机器翻译的发展，还可以加速整个互联网机器翻译的发展。据脸书内测显示，迄今为止，这项技术比其他方法更有效率，最后还可以使翻译的内容更清楚明确。

Facebook’s approach relies on neural² networks, complex mathematical systems that can learn tasks by analyzing³ vast amounts of data. This past fall, Google unveiled a new translation system driven entirely⁴ by neural networks that topped existing models, and many other companies and researchers are pushing in the same direction, most notably⁵ Microsoft and Chinese web giant Baidu.

脸书的方法依赖于神经网络和复杂的数学系统，可以通过分析大量的数据来获悉任务。去年秋天谷歌推出了一款完全由神经网络驱动的新型翻译系统，该系统超过了所有的现有模型，而且许多其他公司及研究人员也在朝着相同的研究方向推进，特别是微软和中国网络巨头百度。

But Facebook is taking a slightly different tack⁶ from most of the other big players. It’s using what are called convolutional neural networks, a technique invented by the venerable researcher Yann LeCun, who now oversees⁷ Facebook’s AI lab. Rather than analyze⁸ a sentence sequentially, one piece at a time, a convolutional neural network can analyze many different pieces at once, before organizing those pieces into a logical hierarchy⁹.

不过脸书采用了一种与其他多数行业巨头略有不同的方法，它使用了一种所谓的卷积神经网络，该技术是由德高望重的研究员扬·勒丘恩发明的，如今勒丘恩管理着脸书的人工智能实验室。卷积神经网络可以同时分析许多不同的内容，然后把这些内容组织成合乎逻辑结构的句子，而不是按顺序一次一段地分析一个句子。

Even if the system is only marginally more accurate than systems like the one Google rolled out in the fall, the company says its technique is more efficient that other neural network-based methods.

虽然这个系统只比谷歌秋季推出的系统略微准确一些，但脸书公司表示，这项技术要比以神经网络为基础的其他方法更有效率。

Others may help push the technique forward as well. Facebook is not only publishing a paper describing its new system but open-sourcing the software engine that drives the system, freely sharing the code with the world at large. It means that translation will evolve far more quickly across the internet—not just on Facebook.

其他人或公司也可以帮助推进这项技术的发展，因为脸书公司不仅发表了介绍其新系统的论文，还公开了驱动系统软件引擎的源代码，免费与全世界分享这个系统的代码。这意味着，不仅仅是脸书公司，整个互联网的翻译技术都会更迅速地发展下去。