Monday, January 16, 2023

Cowboys – Bucs: Brett Maher shaded by dictionary after missed PATs - For The Win - Dictionary

On a night all about Tom Brady, Brett Maher made himself the unfortunate story after making NFL history in the worst way.

During Monday night’s NFL playoff game between the Tampa Bay Buccaneers and the Dallas Cowboys, Maher wrote his way into the history books after becoming the first kicker to miss three extra points in a postseason game. By game’s end, Maher missed four straight extra points — but did make one in the fourth quarter! — an inconceivable figure that bordered on the comedic in the midst of a playoff game.

Things got so bad for Maher that even the Merriam-Webster dictionary was shading his performance!

It’s never a good sign when you’re being dunked on by the dictionary. Yes, it’s clear Maher had the yips during Monday night’s game, but you didn’t have to put him on blast like that, Merriam-Webster!

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Google Translate rolls out offline translation support for 33 new languages - XDA Developers - Translation

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Google Translate rolls out offline translation support for 33 new languages  XDA Developers

StoryWeaver: How A.I. translation tools can help preserve dying languages. - Slate - Translation

Sanjib Chaudhary chanced upon StoryWeaver, a multilingual children’s storytelling platform, while searching for books he could read to his 7-year-old daughter. Chaudhary’s mother tongue is Kochila Tharu, a language with about 250,000 speakers in eastern Nepal. (Nepali, Nepal’s official language, has 16 million speakers.) Languages with a relatively small number of speakers, like Kochila Tharu, do not have enough digitized material for linguistic communities to thrive—no Google Translate, no film or television subtitles, no online newspapers.    In industry parlance, these languages are “underserved” and “underresourced.”

This is where StoryWeaver comes in. Founded by the Indian education nonprofit Pratham Books, StoryWeaver currently hosts more than 50,000 open-licensed stories across reading levels in more than 300 languages from around the world. Users can explore the repository by reading level, language, and theme, and once they select a story, they can click through illustrated slides (each as if it were the page of a book) in the selected language (there are also bilingual options, where two languages are shown side-by-side, as well as download and read-along audio options). “Smile Please,” a short tale about a fawn’s ramblings in the forest, is currently the “most read” story—originally written in Hindi for beginners, it has since been translated into 147 languages and read 281,000 times.

A majority of the languages represented on the platform are from Africa and Asia, and many are Indigenous, in danger of losing speakers in a world of almost complete English hegemony. Chaudhary’s experience as a parent reflects this tension. “The problem with children is that they prefer to read storybooks in English rather than in their own language because English is much, much easier. With Kochila Tharu, the spelling is difficult, the words are difficult, and you know, they’re exposed to English all the time, in schools, on television,” Chaudhary said

Artificial intelligence-assisted translation tools like StoryWeaver can bring more languages into conversation with one another—but the tech is still new, and it depends on data that only speakers of underserved languages can provide. This raises concerns about how the labor of the native speakers powering A.I. tools will be valued and how repositories of linguistic data will be commercialized.

To understand how A.I.-assisted translation tools like StoryWeaver work, it’s helpful to look at neighboring India: With 22 official languages and more than 780 spoken languages, it is no accident that the country is a hub of innovation for multilingual tech. StoryWeaver’s inner core is inspired by a natural language processing tool developed at Microsoft Research India called interactive neural machine translation prediction technology, or INMT.

Unlike most A.I.-powered commercial translation tools, INMT doesn’t do away with a human intermediary altogether. Instead, it assists humans with hints in the language they’re translating into. For example, if you begin typing, “It is raining” in the target language, the model working on the back-end supplies “tonight,” “heavily,” and “cats and dogs” as options for completing your sentence, based on the context and the previous word or set of words. During translation, the tool accounts for meaning in the original language and what the target language allows, and then generates possibilities for the translator to choose from, said Kalika Bali, principal researcher at Microsoft and one of INMT’s main architects.

Tools like INMT allow StoryWeaver’s cadre of volunteers to generate translations of existing stories quickly. The user interface is easy to master even for amateur translators, many of whom, like Chaudhary, are either volunteering their time or already working for nonprofits in early childhood education. The latter is the case for Churki Hansda. Working in Kora and Santali, two underserved Indigenous languages spoken in eastern India, she is an employee at Suchana Uttor Chandipur Community Society, one of StoryWeaver’s many partner organizations scattered all over the world. “We didn’t really have storybooks growing up. Our school textbooks were in Bengali [the dominant regional language], and we would end up memorizing everything because we didn’t understand what we were reading,” Hansda told me. “It’s a good feeling to be able to create books in our languages for our children.”

Amna Singh, Pratham Books’ content and partnerships manager, estimates that 58 percent of the languages represented on StoryWeaver are underserved, a status quo that has cascading consequences for early childhood learning outcomes. But attempts to undo the neglect of underserved language communities are also closely linked with unlocking their potential as consumers, and A.I.-powered translation technology is a big part of this shift. Voice recognition tools and chat bots in regional Indian languages aim to woo customers outside metropolitan cities, a market that is expected to expand as cellular data usage becomes even cheaper.

These tools are only as good as their training data, and sourcing is a major challenge. For sustained multilingualism on the internet, machine translation models require large volumes of training data generated in two languages parallel to one another. Parliamentary proceedings and media publications are common sources of publicly available data that can be scraped for training purposes. However, both these sources—according to Microsoft’s researcher Bali—are too specific, and do not encompass a wide enough range in terms of topics and vocabulary to be properly representative of human speech. (This is why StoryWeaver isn’t a good source for training data, either, because sentences in children’s books are fairly simple and the reading corpus only goes up to fourth-grade reading levels.)

Technical requirements aside, data work is also often invisible and poorly compensated, and it takes place in unregulated environments. There’s increasing concern over what we owe the behind-the-scenes human workers compiling data sets to train A.I. systems.
Known as crowdworkers, these people perform rote, piecemeal tasks that range from labeling images of trees and pedestrians for self-driving cars to spotting signs of disease in medical scans. This type of monotonous “ghost work” takes on an emotional dimension in the context of language preservation. Language data workers contributing to machine translation models are so motivated by the prospect of linguistic dignity on the internet that fair compensation and data stewardship issues get jettisoned in favor of discussions that foreground why this work is important from a cultural perspective.

The cultural value, after all, is enormous: Sanjib Chaudhary’s daughter understands more Kochila Tharu than she did even a few years ago, and Chaudhury’s involvement with StoryWeaver has since grown. Over the past year and a half, he and two friends worked on generating Nepali equivalents for a total of 40,000 English words. But they were paid only $243 for the project, or less than 1 cent per English word, divided three ways. According to Microsoft’s Bali, models need 100,000 paired sentences to start generating acceptable translations.

Despite the repetitiveness and poorly compensated nature of the work, Chaudhary sees himself not as a crowdworker but a language steward. “We have many homophonic words in Kochila Tharu which aren’t there in English. Take the names of different fish … we have so many words for fish, fishing equipment, and fish preparations that you wouldn’t find in other languages,” he said. “If our language dies, we will lose them. I want to collect these words before they disappear.”

The hope for a future when marginal linguistic identities can thrive online is a powerful incentive for stewards like Chaudhary and Hansda. Hansda’s stint with StoryWeaver led to a paid opportunity at AI4Bharat (or A.I. for India), an initiative at the Indian Institute of Technology in Chennai that collects data in labeled pairs for English and 12 Indian languages. The 100,000 sentences Hansda will add to the AI4Bharat dataset for Santali over 18 months span Indigenous oral histories, folktales, literature, sentences, and words. Hansda is paid $1.66 per hour for this work as a “language expert.”

To be truly innovative—and accountable—A.I.-assisted language research must ensure native speakers and their communities aren’t merely contributing data, but also helping to determine what this data will be used for. For now, AI4Bharat seeks to “bring parity with respect to English in A.I. technologies for Indian languages with open-source contributions.” That assumes openness will automatically lead to inclusion. But in practice, there are no clear guidelines preventing companies developing A.I. technologies from using datasets collected and trained by noncommercial research entities like universities or nonprofits. AI4Bharat, for example, categorizes its crowd-sourced datasets as open-source, meaning Hansda’s contributions could be commercialized for profit in the future. There’s precedent for that: Announced last fall, Meta’s not-yet-public Make-a-Video A.I. tool was trained by datasets compiled from publicly available video clips on YouTube and Shutterstock. Calling the practice “A.I. data laundering,” technologist Andy Baio wrote that “Outsourcing the heavy lifting of data collection and model training to non-commercial entities allows corporations to avoid accountability and potential legal liability.”

For now, the push toward linguistic inclusion—whether motivated by commercial profit, social impact, technological innovation, or a mix of all three—is exciting for speakers of minority languages. Hansda hopes for a day when her grandchildren can live their online lives in Santali. “They’ll say, ‘Our grandmother did this,’ ” she said.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society.

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FEMA fires group for nonsensical Alaska Native translations - Yahoo News - Translation

ANCHORAGE, Alaska (AP) — After tidal surges and high winds from the remnants of a rare typhoon caused extensive damage to homes along Alaska’s western coast in September, the U.S. government stepped in to help residents — largely Alaska Natives — repair property damage.

Residents who opened Federal Emergency Management Agency paperwork expecting to find instructions on how to file for aid in Alaska Native languages like Yup’ik or Inupiaq instead were reading bizarre phrases.

“Tomorrow he will go hunting very early, and will (bring) nothing,” read one passage. The translator randomly added the word “Alaska” in the middle of the sentence.

“Your husband is a polar bear, skinny,” another said.

Yet another was written entirely in Inuktitut, an Indigenous language spoken in northern Canada, far from Alaska.

FEMA fired the California company hired to translate the documents once the errors became known, but the incident was an ugly reminder for Alaska Natives of the suppression of their culture and languages from decades past.

FEMA immediately took responsibility for the translation errors and corrected them, and the agency is working to make sure it doesn’t happen again, spokesperson Jaclyn Rothenberg said. No one was denied aid because of the errors.

That’s not good enough for one Alaska Native leader.

For Tara Sweeney, an Inupiaq who served as an assistant secretary of Indian Affairs in the U.S. Interior Department during the Trump administration, this was another painful reminder of steps taken to prevent Alaska Native children from speaking Indigenous languages.

“When my mother was beaten for speaking her language in school, like so many hundreds, thousands of Alaska Natives, to then have the federal government distributing literature representing that it is an Alaska Native language, I can’t even describe the emotion behind that sort of symbolism,” Sweeney said.

Sweeney called for a congressional oversight hearing to uncover how long and widespread the practice has been used throughout government.

“These government contracting translators have certainly taken advantage of the system, and they have had a profound impact, in my opinion, on vulnerable communities,” said Sweeney, whose great-grandfather, Roy Ahmaogak, invented the Inupiaq alphabet more than a half-century ago.

She said his intention was to create the characters so “our people would learn to read and write to transition from an oral history to a more tangible written history.”

U.S. Rep. Mary Peltola, who is Yup’ik and last year became the first Alaska Native elected to Congress, said it was disappointing FEMA missed the mark with these translations but didn’t call for hearings.

“I am confident FEMA will continue to make the necessary changes to be ready the next time they are called to serve our citizens,” the Democrat said.

About 1,300 people have been approved for FEMA assistance after the remnants of Typhoon Merbok created havoc as it traveled about 1,000 miles (1,609 kilometers) north through the Bering Strait, potentially affecting 21,000 residents. FEMA has paid out about $6.5 million, Rothenberg said.

Preliminary estimates put overall damage at just over $28 million, but the total is likely to rise after more assessment work is done after the spring thaw, said Jeremy Zidek, a spokesperson for the Alaska Department of Homeland Security and Emergency Management.

The poorly translated documents, which did not create delays or problems, were a small part of efforts to help people register for FEMA assistance in person, online and by phone, Zidek said.

Another factor is that while English may not be the preferred language for some residents, many are bilingual and can struggle through an English version, said Gary Holton, a University of Hawaii at Manoa linguistics professor and a former director of the Alaska Native Language Center at the University of Alaska Fairbanks.

Central Alaskan Yup’ik is the largest of the Alaska Native languages, with about 10,000 speakers in 68 villages across southwest Alaska. Children learn Yup’ik as their first language in 17 of those villages. There are about 3,000 Inupiaq speakers across northern Alaska, according to the language center.

It appears the words and phrases used in the translated documents were taken from Nikolai Vakhtin’s 2011 edition of “Yupik Eskimo Texts from the 1940s,” said John DiCandeloro, the language center's archivist.

The book is the written record of field notes collected on Russia’s Chukotka Peninsula across the Bering Strait from Alaska in the 1940s by Ekaterina Rubtsova, who interviewed residents about their daily life and culture for a historical account.

The works were later translated and made available on the language center’s website, which Holton used to investigate the origin of the mistranslated texts.

Many of the languages from the area are related but with differences, just as English is related to French or German but is not the same language, Holton said.

Holton, who has about three decades experience in Alaska Native language documentation and revitalization, searched the online archive and found “hit after hit,” words pulled right out of the Russian work and randomly placed into FEMA documents.

“They clearly just grabbed the words from the document and then just put them in some random order and gave something that looked like Yup’ik but made no sense,” he said, calling the final product a “word salad.”

He said it was offensive that an outside company appropriated the words people 80 years ago used to memorialize their lives.

“These are people’s grandparents and great-grandparents that are knowledge-keepers, are elders, and their words which they put down, expecting people to learn from, expecting people to appreciate, have just been bastardized,” Holton said.

KYUK Public Media in Bethel first reported the mistranslations.

“We make no excuses for erroneous translations, and we deeply regret any inconvenience this has caused to the local community,” Caroline Lee, the CEO of Accent on Languages, the Berkeley, California-based company that produced the mistranslated documents, said in a statement.

She said the company will refund FEMA the $5,116 it received for the work and conduct an internal review to ensure it doesn’t happen again.

Lee did not respond to follow-up questions, including how the mistaken translations occurred.

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Sunday, January 15, 2023

Quiz: What Queer Book in Translation Should You Read? - www.autostraddle.com - Translation

Expand your queer reading horizons and try a book in translation! Not sure where to start? Take this quiz! I’ve gathered eight LGBTQ books in translation from around the world in various genres and forms. Manga? Check! Science fiction? Check! Family saga? Check! Magical realism? Check! And more! For other ideas for queer books in translation, check out this list of Must-Read Queer Books from Around the World on Book Riot and this list of Queer Nordic and Scandinavian Books by yours truly at Autostraddle.



Before you go! It costs money to make indie queer media, and frankly, we need more members to survive 2023As thanks for LITERALLY keeping us alive, A+ members get access to bonus content, extra Saturday puzzles, and more! Will you join? Cancel anytime.

Join A+!

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50 Times Signs Were So Hilariously Translated, People Just Had To Share Them Online - Bored Panda - Translation

When there’s language there’s always a chance of getting lost in translation. And while in some instances this can be truly annoying and make your trip to the Far East somewhat of a bummer, in other cases, it gives us all a perfect source of entertainment.

Like these hilariously mistranslated signs that seem to have no shame or no awareness of whatever is wrong about them. So let’s fasten your seatbelt, we are about to get bamboozled. Psst! More poorly translated signs await in our previous post right here.

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Saturday, January 14, 2023

Read the English translation of Shakira's savage Bizarrap lyrics in which she drags Gerard Piqué - PopBuzz - Translation

12 January 2023, 11:29 | Updated: 12 January 2023, 14:25

Shakira puts Gerard Piqué on blast in her brutal 'Bzrp Music Sessions, Vol. 53' lyrics.

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Shakira is back and she is dragging her ex Gerard Piqué in the most iconic way with her 'Bzrp Music Sessions, Vol. 53' lyrics.

Shakira fans will already know that the singer was in a longterm relationship with Spanish footballer Gerard Piqué. They also had two sons together. In 2022, the couple announced that they had split after 11 years. Now, Shakira is airing out all their dirty laundry in the savage lyrics of her brand new Bizarapp collaboration 'Shakira: Bzrp Music Sessions, Vol. 53'.

The new Spanish song is so popular that the video has already been viewed over 24 million times in 10 hours. What do the lyrics mean though? We're here to provide you with the English translation of 'Shakira: Bzrp Music Sessions, Vol. 53'.

READ MORE: Read the English translation of Selena Gomez's De Una Vez lyrics

Shakira Bizarrap lyrics English translation: Bzrp Music Sessions, Vol. 53
Shakira Bizarrap lyrics English translation: Bzrp Music Sessions, Vol. 53. Picture: Dale Play, David Ramos/Getty Images

In the chorus, Shakira sings: "A she-wolf like me ain't for dudes like you / I've outgrown you and that's why you're with a girl just like you." However, it's the verses where she really lets rip. Shakira makes clear: "I do this for you to mortify, chew and swallow, swallow and chew / I'm not coming back to you, even if you're crying or begging."

Shakira then appears to accuse Piqué of being behind her ongoing tax fraud case: "You left me as a neighbor to the mother-in-law / With the press at the door and the debt at the IRS / You thought you'd hurt me, but you made me tougher / Women don't cry anymore, women invoice". Piqué was previously found guilty of tax fraud in 2019.

In the second verse, Shakira takes aim at Piqué's new 22-year-old girlfriend Clara Chia Marti. She sings: "No hard feelings, baby, I wish you the best with my supposed replacement / I'm worth two 22's / You traded a Ferrari for a Twingo / You traded a Rolex for a Casio."

Shakira also quips: "You're going fast, slow down / Ah, a lot of gym / But work-out your brain a little too." She ends the single signings: "It's a wrap / That's it, bye".

Shakira 1, Piqué 0. You can read the full translation below.

Bizarrap & Shakira - 'Shakira: Bzrp Music Sessions, Vol. 53': English Translation

INTRO
(For dudes like you, uh-uh-uh-uh-uh)
Oh-oh (Oh-oh)
(For dudes like you, uh-uh-uh-uh-uh)
Sorry, I already took another plane
I'm not coming back here, I don't want another disappointment
So much that you pretend to be a champion
And when I needed you, you gave your worst version
Sorry, baby, it's been a while
I should have thrown that cat away
A she-wolf like me ain't for a rookie

CHORUS
A she-wolf like me ain't for dudes like you, uh-uh-uh-uh
For dudes like you, uh-uh-uh-uh-uh
I've outgrown you and that's why you're with a girl just like you, uh-uh-uh-uh-uh
Oh, oh

VERSE 1
I do this for you to mortify, chew and swallow, swallow and chew
I'm not coming back to you, even if you're crying or begging
I understood that it's not my fault you're criticized
I only make music, I'm sorry I splashed you
You left me as a neighbour to the mother-in-law
With the press at the door and the debt at the IRS
You thought you'd hurt me, but you made me tougher
Women don't cry anymore, women invoice

PRE-CHORUS
He's got a good person's name
Clearly, it's not what it sounds like
He's got a good person's name
Clearly

CHORUS
She's just like you, uh-uh-uh-uh-uh
For dudes like you, uh-uh-uh-uh-uh
I've outgrown you and that's why you're with one just like you, uh-uh-uh-uh-uh
Oh, oh

VERSE 2
From love to hate, there's only one step
This way don't come back, listen to me
No hard feelings, baby, I wish you the best with my supposed replacement
I don't even know what happened to you
You're so weird that I can't even tell you apart
I'm worth two 22's
You traded a Ferrari for a Twingo
You traded a Rolex for a Casio
You're going fast, slow down
Ah, a lot of gym
But workout your brain a little too
Pictures wherever I am
I feel like a hostage here, it's all right with me
I'll let you go tomorrow and if you want to bring her along, bring her along too

PRE-CHORUS
He's got a good person's name (Uh-uh-uh-uh-uh)
Clearly, it's not what it sounds like (Uh-uh-uh-uh-uh)
He's got a good person's name (Uh-uh-uh-uh-uh)

CHORUS
And a she-wolf like me ain't for dudes like you, uh-uh-uh-uh-uh
For dudes like you, uh-uh-uh-uh-uh
I've outgrown you and that's why you're with a girl just like you, uh-uh-uh-uh-uh
Oh-oh, oh-oh

OUTRO
Uh-uh-uh-uh-uh (For dudes, fo-fo-for dudes like–)
For dudes like you, uh-uh-uh-uh-uh (For dudes, fo-fo-for dudes like–)
I've outgrown you and that's why you're with a girl just like you, uh-uh-uh-uh-uh
It's a wrap
Oh, oh
That's it, bye

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