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.
當(dāng)你的臉書動(dòng)態(tài)消息中出現(xiàn)了一種你不懂的語(yǔ)言，你可以點(diǎn)擊一個(gè)按鈕迅速翻譯這些內(nèi)容。臉書提供了一種方法使你不僅可以與數(shù)百萬(wàn)會(huì)講你的語(yǔ)言的人交流，還可以與數(shù)百萬(wàn)講其他語(yǔ)言的人交流?；蛘咧辽倏梢哉f(shuō)臉書幾乎做到了。

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 efficiently than other methods, which could eventually lead to even sharper translations.
今天上午臉書公司人工智能中心實(shí)驗(yàn)室發(fā)表了一篇論文，介紹了一種可以加速機(jī)器翻譯發(fā)展的新技術(shù)，這種技術(shù)不僅可以加速臉書公司機(jī)器翻譯的發(fā)展，還可以加速整個(gè)互聯(lián)網(wǎng)機(jī)器翻譯的發(fā)展。據(jù)臉書內(nèi)測(cè)顯示，迄今為止，這項(xiàng)技術(shù)比其他方法更有效率，最后還可以使翻譯的內(nèi)容更清楚明確。

Facebook’s approach relies on neural networks, complex mathematical systems that can learn tasks by analyzing vast amounts of data. This past fall, Google unveiled a new translation system driven entirely by neural networks that topped existing models, and many other companies and researchers are pushing in the same direction, most notably Microsoft and Chinese web giant Baidu.
臉書的方法依賴于神經(jīng)網(wǎng)絡(luò)和復(fù)雜的數(shù)學(xué)系統(tǒng)，可以通過(guò)分析大量的數(shù)據(jù)來(lái)獲悉任務(wù)。去年秋天谷歌推出了一款完全由神經(jīng)網(wǎng)絡(luò)驅(qū)動(dòng)的新型翻譯系統(tǒng)，該系統(tǒng)超過(guò)了所有的現(xiàn)有模型，而且許多其他公司及研究人員也在朝著相同的研究方向推進(jìn)，特別是微軟和中國(guó)網(wǎng)絡(luò)巨頭百度。

But Facebook is taking a slightly different tack 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 oversees Facebook’s AI lab. Rather than analyze 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 hierarchy.
不過(guò)臉書采用了一種與其他多數(shù)行業(yè)巨頭略有不同的方法，它使用了一種所謂的卷積神經(jīng)網(wǎng)絡(luò)，該技術(shù)是由德高望重的研究員揚(yáng)·勒丘恩發(fā)明的，如今勒丘恩管理著臉書的人工智能實(shí)驗(yàn)室。卷積神經(jīng)網(wǎng)絡(luò)可以同時(shí)分析許多不同的內(nèi)容，然后把這些內(nèi)容組織成合乎邏輯結(jié)構(gòu)的句子，而不是按順序一次一段地分析一個(gè)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.
雖然這個(gè)系統(tǒng)只比谷歌秋季推出的系統(tǒng)略微準(zhǔn)確一些，但臉書公司表示，這項(xiàng)技術(shù)要比以神經(jīng)網(wǎng)絡(luò)為基礎(chǔ)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幫助推進(jìn)這項(xiàng)技術(shù)的發(fā)展，因?yàn)槟槙静粌H發(fā)表了介紹其新系統(tǒng)的論文，還公開(kāi)了驅(qū)動(dòng)系統(tǒng)軟件引擎的源代碼，免費(fèi)與全世界分享這個(gè)系統(tǒng)的代碼。這意味著，不僅僅是臉書公司，整個(gè)互聯(lián)網(wǎng)的翻譯技術(shù)都會(huì)更迅速地發(fā)展下去。

（翻譯：Dlacus）

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