Breaking the Language Barrier to Get New Immigrants to Vote

Volunteers with Asian Americans Advancing Justice, an Los Angeles-based non-profit, work the phones to get Asian-American voters to the polls in California. The phone bank is part of the “Your Vote Matters” campaign, an effort to get 30,000 infrequent voters to cast their ballots.
A Chinese restaurant during the lunch rush isn’t the first place you’d expect to find a campaign event. But here’s Republican congressional candidate Carl DeMaio, working the banquet room at the China Max restaurant in San Diego and answering questions from local Chinese and Vietnamese reporters.

“The Asian community, all communities, want the same thing,” DeMaio tells a small group of supporters. “We should be focused on what unites us, not divides us.”

The event is one of several DeMaio has held targeting Asian Americans. It’s not a voting bloc that Republicans historically court, but DeMaio is in a dead heat against incumbent Democrat Scott Peters. He knows there are plenty of independents in this room who might help him win this election.

“This is a toss-up seat,” says Jacqui Nguyen. “It’s registered one-third Republican, one-third Democrat, one-third decline-to-state. Within that registration, there’s between 18 to 20 percent Asian American votes out there. That’s a lot of votes that both candidates can try to grab.”

Asian and Pacific Islanders now represent nearly nine million eligible voters nationwide, the fastest growing minority group in the country. The voting bloc has doubled in size in the last decade, and there’s an effort to get more of them out to vote.

That’s why the Republican National Committee has hired Nguyen and two other full-time staffers in California to reach out to Asians and Pacific Islanders. Similar staffers, ones plugged into Asian communities, have also been hired in Colorado, Texas, New York and Virginia. The effort is a direct response to the 2012 presidential election, when Asian Americans overwhelmingly voted for President Obama.

Getting these votes out means overcoming cultural differences; many Asian Americans have never voted at all, even in their native countries.

“They come from countries where democracy, this kind of a voting process, isn’t part of their natural culture,” says Tanzila Ahmed of Asian Americans Advancing Justice, a civic enagement group. “They have to learn about what it means to be a voter. It’s a very new process for them.”

But more than anything else, it’s about breaking breaking down the language barrier. More than a third of Asian-American voters aren’t fluent in English, and they don’t share a common language. That makes Asian American voters hard to reach for many candidates.

“It’s too difficult for them to translate the mailers, it’s too difficult to try and find volunteers that speak their language,” Ahmed says.

She points out that that “a lot of what it means to be engaged in the political process in the US is getting materials from candidates and campaigns because they’re trying to win your vote.” Many Asian Americans simply don’t get that opportunity at all.

So Ahmed is running a phone bank that uses young student volunteers to answer questions about voting in 17 different languages, including Vietnamese, Bengali, Urdu and Mandarin. The goal is to get roughly 30,000 registered voters who don’t usually go to the polls to cast their ballots.


Volunteers at a get-out-the-vote operation in Los Angeles speak 17 different languages, including Chinese, Korean, Tagalog, and Vietnamese. Language barriers are a big factor in low voter turnout for Asian Americans.

Ahmed says she’s noticed that engaging potential voters can be as simple as knowing a greeting in their native language. She often uses an Arabic greeting to engage Muslim voters, and she also likes to share her own story with potential voters.

A few months after September 11, FBI agents came to her parents’ home in California to question her cousin, a young Muslim American. Ahmed says her cousin was singled out because of her family’s religion, even though they stopped attending their mosque and wore patriotic pins to ease suspicions.

She remembers what her mom said back then: “She said to me, ‘It doesn’t matter how long I’ve been in this country, or that I have my citizenship. I’m always going to be treated as a second-class citizen,’” Ahmed says. “That’s a message I take with me whenever I do voter engagement work.”

As Ahmed becomes more politically active, she’s seen the tide turn. California’s online voter registration site added eight new Asian languages this year, including Hindi, Tagalog and Thai.

That may help increase participation in races this November, but the real test will come in two years. With a presidential election on the ballots, candidates will amp up efforts to bring out historically overlooked voters who, hopefully, will influence how the country chooses its next leader.

Graphic_AsianVoters_final


The US Census Bureau reports that while Asian Americans are one of the fastest growing communities in the country, their voter turnout, among eligible voters, is dramatically lower compared to other ethnic groups.

Original article published here: PRI

Combining software with human intelligence to improve translation

Translation graphic

Stanford computer science graduate student Spence Green is first author on two studies about blending human and machine intelligence for language translations.

Computer scientists at Stanford have created a language translation system that allows bilingual humans to translate text faster and more accurately than is currently possible.

This hybrid approach, which blends human and machine intelligence, is aimed at the $34-billion-a-year worldwide market for professional translation services.

The work is the focus of a dissertation by Stanford computer science graduate student Spence Green and is part of a machine translation research effort led by Christopher Manning, a professor of linguistics and of computer science.

The authors presented their hybrid approach to translation at two computer science conferences in October.

As Manning explained, software-based language translation was among the first applications tested on the computers developed during and after World War II.

“During the Cold War there was a great desire to understand the other side and doing translation by computer seemed a natural parallel to computer-based cryptography, which had been successful,” he said.

Christopher ManningChristopher Manning, Stanford professor of linguistics and of computer science, is leading research into computer-assisted translation.

But while computers were great at breaking codes based on rules, they proved unable to resolve the ambiguity inherent in human languages. In recent years, however, advances in machine learning, coupled with the proliferation of text on the Internet, have helped improve fully automatic machine translation. Today, online tools like Google Translate can be used with foreign text to get the gist of the meaning.

But when precise and nuanced translations are needed, such as in preparing international business contracts, firms generally hire bilingual human translators.

Yet even professional translators use machine-based systems as a starting point in a two-step process. First the human uses automatic machine translation to create a rough translation. Then the human translator performs a post-editing step to render an accurate final version.

The Stanford approach combines both steps – the quick machine effort and the nuanced human translation – into one faster and more efficient solution.

“Our system augments the human translator and increases efficiency, accuracy and productivity,” said Green, who was first author on the research papers.

A human translator translates an average of 2,800 words per day, or about seven single-spaced pages.

To improve that productivity, the Stanford system provides the human translator with an interface that resembles a word processor. When the translator begins typing a translation, the system generates suggestions for key words and phrases that could have multiple meanings. Crucially, the system revises its suggestions in real-time as the translator works, fine-tuning the software and improving the quality of its assistance.

“A key challenge is to design an interface in which people and machines can work together to produce a translation,” said Jeffrey Heer, a former Stanford faculty member who was involved in the project. “How should the machine suggest edits without interrupting or overwriting a person’s work? How can a person guide the machine towards better translations?”

Martin Kay, a professor of linguistics at Stanford and an expert on computational translation, framed the nature of the challenge in his seminal 1980 paper, “The Proper Place of Men and Machines in Language Translation.”

“When I wrote this article, I was convinced of two things concerning computers and translation,” Kay said in commenting on the new hybrid system. “One was that there would surely be many roles that computers would be able to fill; second, that automating the whole process would not likely be one of those roles.”

Kay, who was not directly involved in this project, called the new hybrid system “a magnificent example of how human-computer cooperation might be engineered.”

Other team members include Heer, now a professor of computer science at the University of Washington, and Stanford graduate students Jason Chuang, Sida Wang and Sebastian Schuster.

The authors describe their hybrid system in two scholarly papers.

At the User-Interface Software and Technology conference on Oct. 5, they presented a paper on their interface design, which resulted in higher quality final translations than standard post-editing systems.

At the Empirical Methods in Natural Language Processing conference on Oct. 26, the team showed how the machine translation system can use feedback from the human translator to correct mistakes and even adapt to his or her style. This novel advance could lead to personalized machine translation, where each translator has a custom system that learns his or her individual preferences.

Original article published here: Stanford News