Sunday, November 26, 2023

Michael Alexander, poet and broadcaster whose Anglo-Saxon translations were used in Kenneth Clark's Civilisation ... - The Telegraph - Translation

Michael Alexander, who has died aged 82, was a translator, poet, academic and broadcaster whose scholarship was both deep and varied; his interests ranged from Old English poetry to the modernism of Ezra Pound, and he was also the author of an epic history of English literature that ran to more than 400 pages.

While still a student at Oxford University, Alexander started translating Anglo-Saxon poetry into modern English verse, inspired by Ezra Pound’s translation of The Seafarer. In 1966 Penguin published his translations as The Earliest English Poems, and he was subsequently commissioned to translate the 3,182-line Anglo-Saxon poem Beowulf into modern verse.

It was first published in 1973 and Alexander went on to produce a glossed text, also for Penguin, in 1995. “Alexander’s translation is marked by a conviction that it is possible to be both ambitious and faithful,” noted the medievalist Tom Shippey. “[He] communicates the poem with a care which goes beyond fidelity-to-meaning and reaches fidelity of implication.”

Several more books followed, including a History of Old English Literature (Macmillan, 1983) and The Canterbury Tales – The First Fragment (Penguin, 1996). Collectively, Alexander’s Old English books for Penguin sold more than a million copies. His translations were singled out by WH Auden, Seamus Heaney and Kenneth Clark, who used Alexander’s renderings of Anglo-Saxon poetry in Civilisation.

Academic success led to literary commissions for BBC radio, to which Alexander brought his customary erudition and good humour. For 17 years he represented Scotland on Radio 4’s Round Britain quiz, alongside the Sunday Herald journalist Alan Taylor, who cheerfully referred to the programme as “the mental equivalent of the mediaeval rack”. In later years, Alexander’s documentaries for Radio 4 included Past Perfect, a profile of Penelope Fitzgerald, and Macavity’s Not There, on TS Eliot.

Upon his retirement from the position of Berry Professor at the University of St Andrews in 2003, Professor Robert Crawford, Head of the School of English, paid him a warm tribute. “[Professor Alexander] writes deftly, with a fluency born of industry, yet which seems, for all its freight of learning, stylishly and easily airborne,” he observed. “Poetry seems written in his stars.”

The eldest of three children, Michael Joseph Alexander was born in Wigan on May 21 1941, to Joseph Alexander and his wife Winifred, née Gaul. The family lived in Liverpool but had transferred to Wigan after the city came under heavy bombardment from the Germans and the maternity hospital where Winifred had been due to give birth was hit.

Michael Alexander in Australia, 1977 Credit: Lucy Alexander

When Michael was five the Alexanders moved from Liverpool to rural Worcestershire, where Joseph was the manager of Worcestershire Farmers, an agricultural cooperative that made and sold animal feed to farmers. Michael attended boarding school from the age of eight, at Worth Priory in Sussex and then Downside in Somerset. At Trinity College, Oxford, he read English from 1959-62.

After leaving Oxford, he spent a year learning French at Cahors and Italian at Perugia (where he met Ezra Pound several times), then took a job as a general trainee in publishing at William Collins. He left in 1965 for a PhD at Princeton, which he abandoned after a year, finding it oppressively Presbyterian and stiff after Oxford (it was a “dry” campus).

The publication of The Earliest English Poems in 1966 led to a job as a lecturer in English at the University of California at Santa Barbara. Alexander lived in Montecito and was (for the only time in his life) very well paid, but found UCSB rather “vacant”. He went back to London and worked briefly for the publisher André Deutsch, under Diana Athill, at the same time taking on the commission to translate Beowulf for Penguin. 

The work took him to the University of East Anglia, where he held a temporary teaching post, and then to Stirling in Scotland, where he rented a room in a castle, made lifelong friends and met his first wife, Eileen McCall. In 1985 he was appointed to the Berry Chair of English at St Andrews University, where he helped to revitalise the struggling English department.

Michael Alexander in Ireland, 2012 Credit: Lucy Alexander

Following his retirement from St Andrews he continued to write, publishing Medievalism: the Middle Ages in Modern England (2007), Geoffrey Chaucer (2012) and Reading Shakespeare (2013). A History of English Literature (2000) ran to two further editions in 2007 and 2013. In 2021 Shoestring Press published Alexander’s short book of poems Here at the Door (the title taken from a line by John Donne). It included a three-stanza reduction of Beowulf, which ended:

Much later a Dragon awoke,
Sent Beowulf’s hall up in smoke,
So his fifty not-out
Was all up the spout.
But he killed it, then died. What a bloke!

A gifted raconteur, Michael Alexander was capable of discoursing on an enormous range of subjects, often in places where the listener least expected it: supermarket car parks, say, or the queue for the bathroom. He took his Catholic faith very seriously but wore it lightly, and was never dogmatic.

He enjoyed games but didn’t play to win, preferring to explore the dead-end corridors of the Cluedo mansion rather than enter any rooms. He was physically active well into later life, demonstrating the playground zipwire to his granddaughters and playing real tennis at the Oxford University Tennis Club.

With his first wife Eileen, née McCall, Michael Alexander had a son and two daughters. She died of cancer in 1986 and he married, secondly, Mary Sheahan, who survives him, along with the children.

Michael Alexander, born May 21 1941, died November 5 2023

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Two pro-Palestine protesters arrested in London after police could not verify translation of banner - Arab News - Translation

LONDON: Two women demonstrating at a pro-Palestine protest in London on Saturday were arrested for holding a sign containing Arabic writing that police officers could not immediately translate.

The women were asked to translate their sign, which they did, but the Metropolitan Police arrested them after the organization could not verify the translation without an independent translator at the scene, Sky News reported.

In a video which captured the incident, the police asked one of the women to translate her banner, to which she replied: “Who will roll up their sleeves for heaven?”

As the police could not verify her translation through an independent translator, the women were arrested on suspicion of a racially aggravated public order offense and taken to a police station for questioning.

The incident took place at a Hizb ut-Tahrir protest at the Egyptian Embassy on South Street in Mayfair, which was attended by hundreds of people.

Tens of thousands of protesters in London took part in a larger march on Saturday that stretched from Park Lane to Whitehall. They demanded a permanent ceasefire a day after the exchange of hostages held in Gaza for prisoners held in Israel amid a four-day temporary truce.

Police said that while the majority of people protested peacefully across the capital, 18 people were arrested, including at least five who were detained on suspicion of inciting racial hatred.

Officers handed out leaflets during the march that sought to clarify what would be deemed a criminal offense, after the Metropolitan Police faced pressure from senior government officials to be tougher on alleged displays of antisemitism at the protests.

“Anyone who is racist or incites hatred against any group should expect to be arrested, as should anyone who supports Hamas or any other banned organization,” said Deputy Assistant Commissioner Ade Adelekan.

“We will not tolerate anyone who celebrates or promotes acts of terrorism — such as the killing or kidnap of innocent people — or who spreads hate speech.”

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How a dictionary came to spark outrage among the web's otaku - The Japan Times - Dictionary

On Oct. 23, publisher Sanseido did what it would usually do for an upcoming title: It uploaded the basic details of the book, along with sample pages, to its website. It then issued a press release for it, the Otaku Yogo Jiten Daigenkai, or Otaku Dictionary Daigenkai, compiled by students of Nagoya College and headed by Japanese literature researcher Yoshiko Koide.

By that afternoon, netizens were up in arms.

“There is not even a bare-minimum level of correctness that a publicly published book should have,” said one X user in a post that has been viewed 10,000 times. Another posted, “Publishing such subjective, dōjinshi (self-published magazine)-quality work and calling it a ‘dictionary’ is just going to decrease the credibility of Sanseido so they should really stop.”

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Saturday, November 25, 2023

Genius English Translations – Kali Uchis & KAROL G - Labios Mordidos (English Translation) - Genius - Translation

[Intro: Kali Uchis]
A tremendous killer with a flow to slay
Everyone's watching, but she couldn't care less
KAROL and Kali Uchis
The perfect combo to forget about that pain

[Chorus: Kali Uchis]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Verse 1: Kali Uchis]
Once I turn it on, I never stop, stop (Never stop)
Be careful, I don't talk, I shoot (I shoot)
If you still don't know, let me make it clear, clear (Clear)
Playing with me always comes at a cost
Soft Reggaetón, bitten lips
Diamonds trailing down my belly button
More than one is already lost
A doll from a Tarantino movie (Give it to me, papi)
Go easy, you've been warned (Oh-oh)
It's very likely you'll get addicted to me (Oh-oh)
And if you want what's forbidden (Oh-oh)
I'll give it to you hard, darling, I'll punish you (Oh-oh)

[Chorus: Kali Uchis]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Verse 2: Kali Uchis]
Look, I'm soft like honey and coconut
Always rich and sweet like corn arepas
And just with my look, she got all wet up
Your girlfriend goes crazy when I arrive (I arrive)
Maria, Jenny, Catalina, and Sonia
I love my Brazilians and my Colombians (Prr)
Dominicans, Puerto Ricans, I love my Mexicans
And tonight, I'm a lesbian, you make me feel like it

[Chorus: KAROL G, Kali Uchis]
Uh (Oh, oh, oh, oh; ay, papi)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you; come get it)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh (Real Hard)

[Verse 3: KAROL G]
The baby is aggressive with that cute face (Hey)
She's well-established in her whole neighborhood
Short skirt and gistro leaning out of the sunroof
Her ass leaves everyone on mute
Strawberry gloss to bring it down (Bring it down)
Quietly so that no one knows (Ah)
Show me what you have there for me to try it
I'm already feeling hot, come and join me
Strawberry gloss to bring it down (Bring it down)
Quietly so that no one knows (Ah)
She undressed, and I couldn't stop looking at her
That tattoo on her back leaves me breathless

[Chorus: Kali Uchis, KAROL G, Kali Uchis & KAROL G]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is cooler than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Outro: KAROL G, Kali Uchis]
Hey, mami, how good does that tattoo look on you
How far does it go? Show me, let me see
Jajaja
Re-Re-Reggaetón

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When Canola Was a New Word - The Atlantic - Dictionary

This is an edition of Time-Travel Thursdays, a journey through The Atlantic’s archives to contextualize the present, surface delightful treasures, and examine the American idea. Sign up here.

You can tell a lot about a cultural moment by the words it invents. New phenomena, products, social movements, and moods require new language, and an idea without a name is unlikely to stick. The job of a dictionary is to be responsive—but not too reactive—to these trends, to catalog the new ways people are talking, which of course is the new ways they’re thinking. (Among others this year: generative AI, girlboss, meme stock, doomscroll.) Language conjures moments, but it also creates them.

For about a decade starting in January 1987, this magazine’s back page belonged intermittently to Word Watch, a column by Anne H. Soukhanov. Soukhanov was then an editor of The American Heritage Dictionary, and Word Watch was a catalog of terms the dictionary’s editors were tracking for possible inclusion in upcoming editions, based on mentions in the press and pop culture—a sort of first pass at the linguistic infrastructure of tomorrow, an educated guess at how we might describe the unknowable future.

Now that we’re in the future those editors were guessing about, many of the column’s selections feel inevitable: infomercial, Astroturf, zine, NIMBY, ’roid rage, restorative justice. In January 1987, three and a half decades before we had girl dinner, the inaugural Word Watch had graze: “to eat various appetizers … as a full meal.” In October 1989, Soukhanov described in detail a new game called paintball, “dedicated players” of which were apparently called splatmasters, and in February 1991, she noted the rise of “precious language and luscious photographs used to depict recipes or meals,” which she called gastroporn (close enough). Two months later, a word to watch was canola, as in the seed that makes the oil that is almost definitely sitting in your kitchen right now, but of which, back then, “U.S. farmers [had] yet to commit themselves to extensive planting.”

Other times, Word Watch feels like a museum of bad ideas and forgotten trends, which of course is even more entertaining. In January 1988, Word Watch defined blendo as “a style of interior decoration that mixes hightech, Eurostyle, and antique furnishings into an integrated, individualistic whole.” In June 1989, there was halter-top briefs, which I regret to inform you is “a woman’s sleeveless upper garment constructed from men’s knitted, close-fitting briefs,” and which at least one fashion writer predicted would be soon be “‘seen on streets, in stores, and in shopping malls everywhere.’”

In April 1991, the column noted the possible rise of the washing emporium, “a coin-operated laundry incorporating such features as a bar, a restaurant, entertainment, a fax machine, mailboxes, a photocopier, a snack bar, a dining room, and a study area.” It cited as evidence Rutland, Vermont’s Washbucklers, whose owner was quoted in The Boston Globe and then in Word Watch saying that his business “may prove to be ‘the new social center of the ’90s.’” A personal favorite of mine is Skycar, a car-size aircraft that would purportedly fly at altitudes up to 30,000 feet and take off and land vertically, into a parking spot. It would sell for just under $1 million in 1992 dollars, but, Soukhanov noted, “the price is expected to drop as production volume increases.”

Committing a new word to the dictionary is a pretty strange act, when you think about it. So is making a magazine. Both are an attempt at describing the world at present, with only the evidence currently available, in the face of certain obsolescence. Every dictionary edition and every magazine issue is out of date shortly after it’s published; this is by design. These things are iterative, meant to be replaced by something better and newer, which of course will then be replaced, too—always never catching up. I like Word Watch for the same reason I like The Atlantic’s archives as a whole: It lays bare the messiness of trying to describe this big, weird, changing world. It’s open-minded about what the future might look like. It makes mistakes. It does its best.

Washbucklers, by the way, is still open. It has solar panels now.

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'Your United States was normal': Has translation tech really made language learning redundant? - Phys.org - Translation

Google Translate
Credit: Unsplash/CC0 Public Domain

Every day, millions of people start the day by posting a greeting on social media. None of them expect to be arrested for their friendly morning ritual.

But that's exactly what happened to a Palestinian construction worker in 2017, when the caption "يصبحهم" ("good morning") on his Facebook selfie was auto-translated as "attack them."

A human Arabic speaker would have immediately recognized "يصبحهم" as an informal way to say "good morning". Not so AI. Machines are notoriously bad at dealing with variation, a key characteristic of all human languages.

With recent advances in automated translation, the belief is taking hold that humans, particularly English speakers, no longer need to learn other languages. Why bother with the effort when Google Translate and a host of other apps can do it for us?

In fact, some Anglophone universities are making precisely this argument to dismantle their language programs.

Unfortunately, language technologies are nowhere near being able to replace human language skills and will not be able to do so in the foreseeable future because machine language learning and human language learning differ in fundamental ways.

How machines learn languages

For machine translation, algorithms are trained on large amounts of texts to find the probabilities of different patterns of words. These texts can be both monolingual and bilingual.

Bilingual training data comes in the form of human-translated parallel texts. These are almost always based on the standard version of the training language, excluding dialects and slang phrases, as in the example above.

Diversity is a characteristic of all human languages, but diversity is a problem for machines. For instance, "deadly" means "causing death" in most varieties of English, and that is what appears in the training data.

The Australian meaning of "excellent" (from Aboriginal English) puts a spanner in the works. If you input "Deadly Awards" into any translation app, what you'll get in your target language is the equivalent of "death-causing awards."

How machines store languages

The internal linguistic diversity of English, as of any other language, is accompanied by great diversity across languages. Each language does things differently.

Tense, number or gender, for example, need to be grammatically encoded in some languages but not in others. Translating the simple English statement "I am a student" into German requires the inclusion of a grammatical gender marking and so will either end up as "I am a male student" or "I am a female student."

Furthermore, some languages are spoken by many people, have powerful nation states behind them, and are well resourced. Others are not.

"Well resourced" in the context of machine learning means that large digital corpora of training data are available.

The lists of language options offered by automated translation tools—like the list of 133 languages in which Google Translate is currently available—erase all these differences and suggest that each option is the same.

AI speaks English

Nothing could be further from the truth. English is in a class of its own, with over 90% of the training data behind large language models being in English.

The remainder comes from a few dozen languages, in which data of varying sizes are available. The majority of the world's 6,000+ languages are simply missing in action. Apps for some of these are now being created from models "pre-trained" on English, which further serves to cement the dominance of English.

One consequence of inequalities in the training data is that translations into English usually sound quite good because the app can draw both on bilingual and monolingual training data. This doesn't mean they are accurate: one recent study found about half of all questions in Vietnamese were incorrectly auto-translated as statements.

Machine-translated text into languages other than English is even more problematic and routinely riddled with mistakes. For instance, COVID-19 testing information auto-translated into German included invented words, grammatical errors, and inconsistencies.

What machine translation can and can't do

Machine translation is not as good as most people think, but it is useful to get the gist of web sites or be able to ask for directions in a tourist destination with the help of an app.

However, that is not where it ends. Translation apps are increasingly used in high-stakes contexts, such as hospitals, where staff may attempt to bypass human interpreters for quick communication with patients who have limited proficiency in English.

This causes big problems when, for instance, a patient's discharge instructions state the equivalent of "Your United States was normal"—an error resulting from the abbreviation "US" being used for "ultrasound" in medical contexts.

Therefore, there is consensus that translation apps are suitable only in risk-free or low-risk situations. Unfortunately, sometimes even a caption on a selfie can turn into a high-risk situation.

We need to cultivate human multilingual talent

Only humans can identify what constitutes a low- or high-risk situation and whether the use of machine translation may be appropriate. To make informed decisions, humans need to understand both how languages work and how machine learning works.

It could be argued that all the errors described here can be ironed out with more training data. There are two problems with this line of reasoning. First, AI already has more training data than any human will ever be able to ingest, yet makes mistakes no human with much lower levels of investment in their language learning would make.

Second, and more perniciously, training machines to do our language learning for us is incredibly costly. There are the well-known environmental costs of AI, of course. But there is also the cost of dismantling language teaching programs.

If we let go of language programs because we can outsource simple multilingual tasks to machines, we will never train humans to achieve advanced language proficiency. Even from the perspective of pure strategic national interest, the skills to communicate across language barriers in more risky contexts of economics, diplomacy or health care are essential.

Languages are diverse, fuzzy, variable, relational and deeply social. Algorithms are the opposite. By buying into the hype that machines can do our language work for us we dehumanize what it means to use languages to communicate, to make meaning, to create relationships and to build communities.

Provided by The Conversation

This article is republished from The Conversation under a Creative Commons license. Read the original article.The Conversation

Citation: 'Your United States was normal': Has translation tech really made language learning redundant? (2023, November 22) retrieved 25 November 2023 from https://ift.tt/AWzucsM

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Friday, November 24, 2023

Genius English Translations – Kali Uchis & KAROL G - Labios Mordidos (English Translation) - Genius - Translation

[Intro: Kali Uchis]
A tremendous killer with a flow to slay
Everyone's watching, but she couldn't care less
KAROL and Kali Uchis
The perfect combo to forget about that pain

[Chorus: Kali Uchis]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Verse 1: Kali Uchis]
Once I turn it on, I never stop, stop (Never stop)
Be careful, I don't talk, I shoot (I shoot)
If you still don't know, let me make it clear, clear (Clear)
Playing with me always comes at a cost
Soft Reggaetón, bitten lips
Diamonds trailing down my belly button
More than one is already lost
A doll from a Tarantino movie (Give it to me, papi)
Go easy, you've been warned (Oh-oh)
It's very likely you'll get addicted to me (Oh-oh)
And if you want what's forbidden (Oh-oh)
I'll give it to you hard, darling, I'll punish you (Oh-oh)

[Chorus: Kali Uchis]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Verse 2: Kali Uchis]
Look, I'm soft like honey and coconut
Always rich and sweet like corn arepas
And just with my look, she got all wet up
Your girlfriend goes crazy when I arrive (I arrive)
Maria, Jenny, Catalina, and Sonia
I love my Brazilians and my Colombians (Prr)
Dominicans, Puerto Ricans, I love my Mexicans
And tonight, I'm a lesbian, you make me feel like it

[Chorus: KAROL G, Kali Uchis]
Uh (Oh, oh, oh, oh; ay, papi)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is tougher than you (Nobody like you; come get it)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh (Real Hard)

[Verse 3: KAROL G]
The baby is aggressive with that cute face (Hey)
She's well-established in her whole neighborhood
Short skirt and gistro leaning out of the sunroof
Her ass leaves everyone on mute
Strawberry gloss to bring it down (Bring it down)
Quietly so that no one knows (Ah)
Show me what you have there for me to try it
I'm already feeling hot, come and join me
Strawberry gloss to bring it down (Bring it down)
Quietly so that no one knows (Ah)
She undressed, and I couldn't stop looking at her
That tattoo on her back leaves me breathless

[Chorus: Kali Uchis, KAROL G, Kali Uchis & KAROL G]
Uh (Oh, oh, oh, oh)
The girl is turned on, sticks to me like a tattoo (Like a tattoo)
I guarantee you that nobody is cooler than you (Nobody like you)
May God bless that ass that rubs, uh-uh
Like a tattoo, uh-uh

[Outro: KAROL G, Kali Uchis]
Hey, mami, how good does that tattoo look on you
How far does it go? Show me, let me see
Jajaja
Re-Re-Reggaetón

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