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Bringing hope to a refugee family, using Google Translate

In 2015, I joined Google to be a part of a company using technology to help others. I’m proud that Google’s commitment to its mission—to organize the world’s information and make it universally accessible and useful—remains strong 20 years in. I knew I wanted to be a part of it all, but had no idea that I would experience the power of our mission firsthand, and that it would help me to forge a friendship when I least expected it.

For the past three years, my wife and I have been working with organizations involved with refugee resettlement efforts. We both have immigrant parents, so we’ve heard stories about resettling in a country to make a better life for your children, but being forced to leave a country is very different. These refugees are often fleeing from life threatening situations. Aside from dealing with their past trauma and being in an unfamiliar place without a support system, they often can’t speak the local language.

My wife and I learned of a family of four—Nour, Mariam, three-year old Sanah and six-month-old Yousuf—who settled in Rialto, 45 minutes from where my wife and I grew up in Southern California. Through the assistance of organizations such as Hearts of Mercy and Miry’s List, they settled into an apartment shortly before giving birth to Yousuf. Still recovering from injuries sustained in Syria, Nour was unable to work, and had to rely on the help of others to get by. Without a car, their options were further limited. Then, in April of this year, they faced their hardest challenge yet: their daughter Sanah was diagnosed with Stage 4 Neuroblastoma.

We wanted to help, but didn’t know where to start—and as new parents ourselves, we could relate on a personal level. We fundraised for the family and collected toys for Yousuf and Sanah in hopes that they could feel supported. Moreso, we wanted to help them get through Sanah’s treatments with as little to worry about as possible.

A few weeks after we first heard of their story, we went to their home to meet in person. Nour was waiting outside for us, and we quickly realized there was a challenge that we had overlooked: the family only spoke Arabic. There I was, face to face with Nour, wanting to hear his story and reassure him that he’s surrounded by a supportive community, but couldn't convey those thoughts or give Nour the ability to convey his. The only option I could think of was Google Translate, which I had used in previous international trips, and hoped would bridge this gap.

I opened the app to translate a few words, but we couldn’t get far by manually typing sentences. Instead, I tried "conversation" mode, which allows for real-time audio translations and makes the interaction feel more natural. We talked about his family’s story and what they were up against. I learned that back in Syria, Nour was shot twice in the back, and endured the deaths of his brothers. Now, Nour and Mariam are giving up everything to take care of Sanah and spend up to two hours commuting on a bus to and from her hospital treatments. Through all of this, they continue to be optimistic and hopeful, and are grateful for being able to make it to America.

image (2).png

A snapshot of my visit with Nour.

I never imagined that we could sustain a 90-minute conversation in two languages, and that it would bring us closer together, inspiring me in a way I didn’t expect. Without Translate, we would have exchanged a few pleasantries, shared poorly communicated words and parted ways. Instead, we walked away with a bond built on an understanding of one another—we were just two fathers, talking about our fears and hopes for our family’s future. To this day, we stay connected on how the family is doing, and I’m looking forward to keeping this relationship going for a long time.

Refugee families often find themselves in situations that may seem normal to you and me—like at the DMV trying to get a driver’s license—or worse, in a dire situation like a hospital, with no way of communicating. We generally think of technology as an enabler of change, driving efficiency or making the impossible happen. But in this case, technology allowed me to make a life-changing connection, and brought me closer to family who was very far away from home.

Source: Translate


Tune in for the world’s first Google Translate music tour

Eleven years ago, Google Translate was created to break down language barriers. Since then, it has enabled billions of people and businesses all over the world to talk, connect and understand each other in new ways.

And we’re still re-imagining how it can be used—most recently, with music. The music industry in Sweden is one of the world's most successful exporters of hit music in English—with artists such Abba, The Cardigans and Avicii originating from the country. But there are still many talented Swedish artists who may not get the recognition or success they deserve except for in a small country up in the north.

This sparked an idea: might it be possible to use Google Translate with the sole purpose of breaking a Swedish band internationally?

Today, we’re presenting Translate Tour, in which up and coming Swedish indie pop group Vita Bergen will be using Google Translate to perform their new single “Tänd Ljusen” in three different languages—English, Spanish and French—on the streets of three different European cities. In just a couple of days, the band will set off to London, Paris and Madrid to sing their locally adapted songs in front of the eyes of the public—with the aim of spreading Swedish music culture and inviting people all over the world to tune into the band’s cross-European indie pop music.

Translate Tour 2_Credit Anton Olin.jpg

William Hellström from Vita Bergen will be performing his song in English, Spanish and French.

Last year Google Translate switched from phrase-based translation to Google Neural Machine Translation, which means that the tool now translates whole sentences at a time, rather than just piece by piece. It uses this broader context to figure out the most relevant translation, which it then rearranges and adjusts to be more like a human speaking with proper grammar.

Using this updated version of Google Translate, the English, Spanish and French translations of the song were close to flawless. The translations will also continue to improve, as the system learns from the more people using it.

Tune in to Vita Bergen’s release event, live streamed on YouTube today at 5:00 p.m. CEST, or listen to the songs in Swedish (“Tänd Ljusen”), English (“Light the Lights”), Spanish (“Enciende las Luces”) and French (“Allumez les Lumières”).

Source: Translate


Making the internet more inclusive in India

More than 400 million people in India use the internet, and more are coming online every day. But the vast majority of India’s online content is in English, which only 20 percent of the country’s population speaks—meaning most Indians have a hard time finding content and services in their language.

Building for everyone means first and foremost making things work in the languages people speak. That’s why we’ve now brought our new neural machine translation technology to translations between English and nine widely used Indian languages—Hindi, Bengali, Marathi, Gujarati, Punjabi, Tamil, Telugu, Malayalam and Kannada.

Neural machine translation translates full sentences at a time, instead of pieces of a sentence, using this broader context to help it figure out the most relevant translation. The result is higher-quality, more human sounding translations.

Just like it’s easier to learn a language when you already know a related language, our neural technology speaks each language better when it learns several at a time. For example, we have a whole lot more sample data for Hindi than its relatives Marathi and Bengali, but when we train them all together, the translations for all improve more than if we’d trained each individually.

NMT Translation India.jpg
Left: Phrase-based translation; right: neural machine translation

These improvements to Google Translate in India join several other updates we announced at an event in New Delhi today, including neutral machine translation in Chrome and bringing the Rajpal & Sons Hindi dictionary online so it’s easier for Hindi speakers to find word meanings right in search results. All these improvements help make the web more useful for hundreds of millions of Indians, and bring them closer to benefiting from the full value of the internet.

Source: Translate


Making the internet more inclusive in India

More than 400 million people in India use the internet, and more are coming online every day. But the vast majority of India’s online content is in English, which only 20 percent of the country’s population speaks—meaning most Indians have a hard time finding content and services in their language.

Building for everyone means first and foremost making things work in the languages people speak. That’s why we’ve now brought our new neural machine translation technology to translations between English and nine widely used Indian languages—Hindi, Bengali, Marathi, Gujarati, Punjabi, Tamil, Telugu, Malayalam and Kannada.

Neural machine translation translates full sentences at a time, instead of pieces of a sentence, using this broader context to help it figure out the most relevant translation. The result is higher-quality, more human sounding translations.

Just like it’s easier to learn a language when you already know a related language, our neural technology speaks each language better when it learns several at a time. For example, we have a whole lot more sample data for Hindi than its relatives Marathi and Bengali, but when we train them all together, the translations for all improve more than if we’d trained each individually.

NMT Translation India.jpg
Left: Phrase-based translation; right: neural machine translation

These improvements to Google Translate in India join several other updates we announced at an event in New Delhi today, including neutral machine translation in Chrome and bringing the Rajpal & Sons Hindi dictionary online so it’s easier for Hindi speakers to find word meanings right in search results. All these improvements help make the web more useful for hundreds of millions of Indians, and bring them closer to benefiting from the full value of the internet.

Source: Translate


Even better translations in Chrome, with one tap

Half the world’s webpages are in English, but less than 15 percent of the global population speaks it as a primary or secondary language. It’s no surprise that Chrome’s built-in Translate functionality is one of the most beloved Chrome features. Every day Chrome users translate more than 150 million webpages with just one click or tap.

Last year, Google Translate introduced neural machine translation, which uses deep neural networks to translate entire sentences, rather than just phrases, to figure out the most relevant translation. Since then we’ve been gradually making these improvements available for Chrome’s built-in translation for select language pairs. The result is higher-quality, full-page translations that are more accurate and easier to read.

Today, neural machine translation improvement is coming to Translate in Chrome for nine more language pairs. Neural machine translation will be used for most pages to and from English for Indonesian and eight Indian languages: Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil and Telugu. This means higher quality translations on pages containing everything from song lyrics to news articles to cricket discussions.
translation.png
From left: A webpage in Indonesian; the page translated into English without neural machine translation; the page translated into English with neural machine translation. As you can see, the translations after neural machine translation are more fluid and natural.

The addition of these nine languages brings the total number of languages enabled with neural machine translations in Chrome to more than 20. You can already translate to and from English for Chinese, French, German, Hebrew, Hindi, Japanese, Korean, Portuguese, Thai, Turkish, Vietnamese, and one-way from Spanish to English.

We’ll bring neural machine translation to even more languages in the future. Until then, learn more about enabling Translate in Chrome in our help center.

Source: Translate


Even better translations in Chrome, with one tap

Half the world’s webpages are in English, but less than 15 percent of the global population speaks it as a primary or secondary language. It’s no surprise that Chrome’s built-in Translate functionality is one of the most beloved Chrome features. Every day Chrome users translate more than 150 million webpages with just one click or tap.

Last year, Google Translate introduced neural machine translation, which uses deep neural networks to translate entire sentences, rather than just phrases, to figure out the most relevant translation. Since then we’ve been gradually making these improvements available for Chrome’s built-in translation for select language pairs. The result is higher-quality, full-page translations that are more accurate and easier to read.

Today, neural machine translation improvement is coming to Translate in Chrome for nine more language pairs. Neural machine translation will be used for most pages to and from English for Indonesian and eight Indian languages: Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil and Telugu. This means higher quality translations on pages containing everything from song lyrics to news articles to cricket discussions.
translation.png
From left: A webpage in Indonesian; the page translated into English without neural machine translation; the page translated into English with neural machine translation. As you can see, the translations after neural machine translation are more fluid and natural.

The addition of these nine languages brings the total number of languages enabled with neural machine translations in Chrome to more than 20. You can already translate to and from English for Chinese, French, German, Hebrew, Hindi, Japanese, Korean, Portuguese, Thai, Turkish, Vietnamese, and one-way from Spanish to English.

We’ll bring neural machine translation to even more languages in the future. Until then, learn more about enabling Translate in Chrome in our help center.

Source: Translate


The Arrival of our 32nd Word Lens Language, Heptapod B

We’re honored to have partnered with Dr. Louise Banks, esteemed linguistics professor, to develop instant camera translation for our 32nd language, Heptapod B. Following our experience with logograms in Chinese and Japanese, as well as the many characters containing circles in Korean, we were ready to blend our expertise in low-memory-footprint convolutional modeling and Dr. Banks’ linguistic background to the deciphering of circular logograms in the Word Lens feature in the Google Translate app.

Translate_Arrival_1.png

The challenge with understanding Heptapod B is its nonlinear orthography. Fortunately, Google's neural machine translation system employs an encoder/decoder system that internally represents sentences as high-dimensional vectors. These vectors map well to the non-linear orthography of the Heptapod language and they are really the enabling technical factor in translating Heptapod B.

We interpret Heptapod B into English, Chinese, Danish, Japanese, Urdu, Russian, French, Spanish and Arabic. As with our other Word Lens languages, it works offline, which is really handy if you happen to need to read a circular logogram in an isolated location. Dr. Banks assures us that the app will continue to work for at least 3,000 years.

Translate_Arrival_2.png

Communicating across language (and glass) barriers can be a rather alienating experience. While learning a new writing system can be quite rewarding and even a mind-altering experience, not everyone has time for that. So whether the world’s fate hangs in the balance, or if you’re simply trying to discern whether your coffee stain ring means something, we wish you success as you integrate this tool into the story of your life.

Source: Translate


The Arrival of our 32nd Word Lens Language, Heptapod B

We’re honored to have partnered with Dr. Louise Banks, esteemed linguistics professor, to develop instant camera translation for our 32nd language, Heptapod B. Following our experience with logograms in Chinese and Japanese, as well as the many characters containing circles in Korean, we were ready to blend our expertise in low-memory-footprint convolutional modeling and Dr. Banks’ linguistic background to the deciphering of circular logograms in the Word Lens feature in the Google Translate app.

Translate_Arrival_1.png

The challenge with understanding Heptapod B is its nonlinear orthography. Fortunately, Google's neural machine translation system employs an encoder/decoder system that internally represents sentences as high-dimensional vectors. These vectors map well to the non-linear orthography of the Heptapod language and they are really the enabling technical factor in translating Heptapod B.

We interpret Heptapod B into English, Chinese, Danish, Japanese, Urdu, Russian, French, Spanish and Arabic. As with our other Word Lens languages, it works offline, which is really handy if you happen to need to read a circular logogram in an isolated location. Dr. Banks assures us that the app will continue to work for at least 3,000 years.

Translate_Arrival_2.png

Communicating across language (and glass) barriers can be a rather alienating experience. While learning a new writing system can be quite rewarding and even a mind-altering experience, not everyone has time for that. So whether the world’s fate hangs in the balance, or if you’re simply trying to discern whether your coffee stain ring means something, we wish you success as you integrate this tool into the story of your life.

(Okay, if you haven’t guessed already... we’re just having some fun here. But we really are eager to bring Word Lens and Neural translation to more languages,
so stay tuned.)

Source: Translate


The Arrival of our 32nd Word Lens Language, Heptapod B

We’re honored to have partnered with Dr. Louise Banks, esteemed linguistics professor, to develop instant camera translation for our 32nd language, Heptapod B. Following our experience with logograms in Chinese and Japanese, as well as the many characters containing circles in Korean, we were ready to blend our expertise in low-memory-footprint convolutional modeling and Dr. Banks’ linguistic background to the deciphering of circular logograms in the Word Lens feature in the Google Translate app.

Translate_Arrival_1.png

The challenge with understanding Heptapod B is its nonlinear orthography. Fortunately, Google's neural machine translation system employs an encoder/decoder system that internally represents sentences as high-dimensional vectors. These vectors map well to the non-linear orthography of the Heptapod language and they are really the enabling technical factor in translating Heptapod B.

We interpret Heptapod B into English, Chinese, Danish, Japanese, Urdu, Russian, French, Spanish and Arabic. As with our other Word Lens languages, it works offline, which is really handy if you happen to need to read a circular logogram in an isolated location. Dr. Banks assures us that the app will continue to work for at least 3,000 years.

Translate_Arrival_2.png

Communicating across language (and glass) barriers can be a rather alienating experience. While learning a new writing system can be quite rewarding and even a mind-altering experience, not everyone has time for that. So whether the world’s fate hangs in the balance, or if you’re simply trying to discern whether your coffee stain ring means something, we wish you success as you integrate this tool into the story of your life.

(Okay, if you haven’t guessed already... we’re just having some fun here. But we really are eager to bring Word Lens and Neural translation to more languages,
so stay tuned.)

Source: Translate


How Google Translate is making learning English fun in Israel

Using neural machine translation, we’ve just updated Hebrew and Arabic languages on Google Translate. But what you can’t see on the surface is that these translations also improved thanks to students across Israel. As English as a Foreign Language (EFL) students used the Google Translate Community platform to learn and practice their English, they actually improved translations for everyone in the process.

Adele Raemer is an Israeli English teacher, a trainer for English as a Foreign Language (EFL) and digital pedagogy at the Israel Ministry of Education; she’s also a Google Certified Innovator, a Google Educator Group leader, and blogger.

Adele Raemer.png
Adele Raemer, English as a Foreign Language teacher and trainer at Israel’s Ministry of Education

When Adele first used the Translate Community as tool to teach English, she was impressed by how eager and motivated her students became. She wanted other students to share in the experience, so with the support of the Ministry of Education EFL superintendent and our education team, she turned this into a challenge for classrooms across Israel. The goal was to help students work on their vocabulary, develop critical thinking and translating skills and enhanced their engagement with English studies.

Last spring, 51 classes from across the country joined our Google Translate Community pilot competition. A month later, the class with the highest number of collective contributions joined us for a visit to our Google Israel office. The teachers used the challenge as a fun activity on top of their regular curriculum. As Mazi, an English teacher at “Hodayot” high school, said: “The experience of participating in the competition was very positive and enriched my teaching. Any time that a student finished a task early or had a bit of time at the end of the lesson, they could be productive by going into the site and translating!”

Translate_Israel_Group.png
Winning class from Jadeidi-Makr science school who won a visit to the Google Israel office

Inspired by the success of Adele's pilot program, the Translate Community team then built new tools that allowed group contributions and measured results more accurately. With new supporting lesson plans, more than 150 classes participated in a three month competition for Hebrew-English and Arabic-English. From these two competitions, 3,500 students translated and verified more than 4 million words and phrases.

Translate_Israel_1.png
English teacher from the winning school, “Nitzanim” school, with a student translating during a lesson

We’ve incorporated this multi-lingual knowledge into the training for our cutting-edge neural technology, which we’ve just launched today for Hebrew and Arabic. That means every one of these contributions helped improve translations for millions of people doing translations to or from these related languages.

We were thrilled to see the great impact that these students had on Translate itself. It’s so cool to see how the next generation of students is working hand in hand with the next generation of machine translation technology!

Source: Translate