Google Translate – Global by Design

Posted on June 7, 2016

Google Translate: Ten Years Later

I remember when Google Translate went live. Hard to believe it was 10 years ago.

I remember thinking that this relatively new technology, known as Statistical Machine Translation (SMT), was going to change everything.

At the time, many within the translation community were dismissive of Google Translate. Some viewed it as a passing phase. Very few people said that machine translation would ever amount to much more than a novelty.

But I wasn’t convinced that this was a novelty. As I wrote in 2007 I believed that the technologists were taking over the translation industry:

SMT is not by itself going to disrupt the translation industry. But SMT, along with early adopter clients (by way of the Translation Automation Users Society), and the efforts of Google, are likely to change this industry in ways we can’t fully grasp right now.

Here’s a screen grab of Google Translate from 2006, back when Chinese, Japanese, Korean and Arabic were still in BETA:

Growth in languages came in spurts, but roughly at a pace of 10 languages per year.

And here is a screen grab today:

Google Translate has some impressive accomplishments to celebrate:

103 languages supported
100 billion words translated per day
500 million users around the world
Most common translations are between English and Spanish, Arabic, Russian, Portuguese and Indonesian
Brazilians are the heaviest users of Google Translate
3.5 million people have made 90 million contributions through the Google Translate Community

The success of Google Translate illustrates that we will readily accept poor to average translations versus no translations at all.

To be clear, I’m not advocating that companies use machine translation exclusively. Machine translation can go from utilitarian to ugly when it comes to asking someone to purchase something. If anything, machine translation has shown to millions of people just how valuable professional translators truly are.

But professional translators simply cannot translate 100 billion words per day.

Many large companies now use machine translation, some translating several billion words per month.

Companies like Intel, Microsoft, Autodesk, and Adobe now offer consumer-facing machine translation engines. Many other companies are certain to follow.

Google’s investment in languages and machine translation has been a key ingredient to its consistent position as the best global website according to the annual Report Card.

Google Translate has taken translation “to the people.” It has opened doors and eyes and raised language expectations around the world.

I’m looking forward to the next 10 years.

Posted on July 17, 2015

In search of a better translation icon

A few years ago I wrote about the translation icon and its many variations at that point in time.

I thought now would be a good time to revisit this icon.

Let’s start with the Google Translate. This icon has not changed in substance over the years but it has been streamlined a great deal.

Here is the icon used for its app:

Microsoft uses a similar icon across its website, apps, and APIs:

I’m not a fan of this icon, despite how prevalent it has become.

Before I go into why exactly, here is another app icon I came across:

These first three icons display specific language pairs, which could be interpreted as showing preference for a given language pair. This is the issue that I find problematic.

Why can’t a translate icon be language agnostic?

Here is how SDL approaches the translation icon:

Although the icon is busy, I’m partial to what SDL is doing here — as this icon does not display a given script pair.

Here is another icon, from the iTranslate app:

The counter-argument to a globe icon is this: It is used EVERYWHERE. And this is true. Facebook, for example, uses the globe icon for notifications, which I’ve never understood. Nevertheless, the globe icon can successfully deliver different messages depending on context. In the context of a mobile app icon, I think a globe icon works perfectly well.

So the larger question here is whether or not a language pair is required to communicate “translation.”

Google and Microsoft certainly believe that a language pair is required, which is where we stand right now. I’d love to see this change. I think we can do better.

Posted on May 15, 2015

The humans behind machine translation

Google Translate is the world’s most popular machine translation tool.

And, despite predictions by many experts in the translation industry, the quality of Google Translate has improved nicely over the past decade. Not so good that professional translators are in any danger of losing work, but good enough that many of these translators will use Google Translate to do a first pass on their translation jobs.

But even the best machine translation software can only go so far on its own. Eventually humans need to assist.

Google has historically been averse to any solution that required lots and lots of in-person human input — unless these humans could interact virtually with the software.

Behind Google’s machine translation software are humans.

In the early days of Google Translate, there were very few humans involved. The feature that identified languages based on a small snippet of text was in fact developed by one employee as his 20% project.

Google Translate is a statistical machine translation engine, which means it relies on algorithms that digest millions of translated language pairs. These algorithms, over time, have greatly improved the quality of Google Translate.

But algorithms can only take machine translation so far.

Eventually humans must give these algorithms a little help.

So it’s worth mentioning that Google relies on “translate-a-thons” to recruit people to help improve the quality.

According to Google, more than 100 of these events have been held resulting in addtion of more than 10 million words:

It’s made a huge difference. The quality of Bengali translations are now twice as good as they were before human review. While in Thailand, Google Translate learned more Thai in seven days with the help of volunteers than in all of 2014.

Of course, Google has long relied on a virtual community of users to help improve translation and search results. But actual in-person events is a relatively new level of outreach for the company — and I’m glad to see it.

This type of outreach will keep Google Translate on the forefront in the MT race.

If you want to get involved, join Google’s Translate Community.

Posted on July 5, 2012

Gabble On: Using machine translation to learn a language

Ethan Shen, who has become quite an expert on the various machine translation (MT) engines, has launched a nifty web service designed to help you improve your language skills: Gabble On.

Basically, the site leverages an MT engine (Google, Bing, Systran) to display a news article in the target language.

It’s still a work in progress, but I like the way it displays source and target sentences side by side so you can follow along sentence by sentence.

I think the site has the greatest potential for teaching vocabulary.

Ethan welcomes input so give it a test drive and tell him what you think!

Posted on January 27, 2011 June 19, 2013

Think your translator is cutting corners? Try the machine translation detector…

Lior Libman of One Hour Translation has released a web tool that you can use to quickly determine if text was translated by one of the three major machine translation (MT) engines: Google Translate, Yahoo! Babel Fish, and Bing Translate.

It’s called the Translation Detector.

To use it, you input your source text and target text and then it tells you the probability of each of the three MT engines being the culprit.

How does it know this? Simple. Behind the scenes it takes the source text and runs it through the three MT engines and then compares the output to your target text. So the caveat here is that this tool only compares against those three MT engines.

Being the geek that I am, I couldn’t help but give it a test drive.

It correctly guessed between text translated by Google Translate vs. Bing Translate (I didn’t try Yahoo!). Below is a screen shot of what I found after inputing the Google Translate text:

Next, I input source and target text that I had copied from the Apple web site (US and Germany). I would be shocked if the folks at Apple were crunching their source text through Google Translate.

And, sure enough, here’s what the Translation Detector spit out:

So if you suspect your translator is taking shortcuts with Google Translate or another engine, this might be just the tool to test that theory.

Though in defense of translators everywhere, I’ve never heard of anyone resorting to an MT engine to cut corners.

I actually see this tool as part of something bigger — the emergence of third-party tools and vendors that evaluate, benchmark, and optimize machine translation engines. Right now, these three engines are black boxes. I wrote awhile back of one person’s efforts to compare the quality of these three engines. But there are lots of opportunities here. As more people use these engines there will be a greater need for more intelligence about which engine works best for what types of text. And hopefully we’ll see vendors arise that leverage these MT engines for industry-specific functions.

UPDATE: As the commenters noted below, there are limits to the quality of results you will get if you input more than roughly 130 words. The tool is limited by API word-length caps.

Posted on August 30, 2009

Google Translate: Now in 51 languages

In February of this year, Google Translate surpassed 40 languages.

Six months later, Google added ten more languages, a two-year growth trajectory illustrated below:

google_translate_languages

Google went from 13 languages to 51 languages in less than 16 months.

Not bad.

And, yes, I’m aware that we must not confuse quantity of translations with quality of translations. Your translation mileage will most certainly vary by language pair. Still, as language pairs go, Google is the only game in town across many.

Here are the 10 most recently added languages:

Albanian
Afrikaans
Belarusian
Icelandic
Irish
Macedonian
Malay
Swahili
Welsh
Yiddish

On a related noted, 41 of these languages are now incorporated into Google Docs.

Posted on May 8, 2009

Have you dined at the Translate Server Error lately?

File this post under Lost in (Machine) Translation.

translate_server_error

This photo arrived courtesy of Gareth Morgan at Neovia Financial.

Apparently the proprietor of this restaurant in China decided to create an English-language sign using machine translation (MT) software and, apparently, the MT engine wasn’t working all that well.

So instead of “restaurant” we have “translate server error.”

It’s certainly one of the more memorable restaurant names I’ve come across. I’ll be sure to look out for it when I visit!

And I’ve love to know which MT engine delivered this message.

Posted on March 25, 2009

Google Translate graduates to the home page

Only a few days after Microsoft announces a widget to bring machine translation into your Web site’s home page, Google takes a step towards integrating machine translation into its home page.

According to the unofficial Google blog, Google has inserted its “translate” link into a number of localized Google sites — such as France and Spain. Google.com is not yet included.

This is just another sign that translation is becoming a core element of Google’s world domination strategy. If you’re curious about Google’s market share around the world — here’s an interesting “crowdsourced” document.

Here is the France home page:

google_translate_fr

I rarely ever use this pull-down menu and I wonder how many others do. I realize that Google strives to keep an austere home page and this is one solution — but I’m not sure it’s worth it. If users can’t find the translation link they may never use it.

Google Translate is no longer growing up, it’s growing out — integrating itself across all of its many properties.