IAN MCEWAN, the award-winning British novelist, is the author of The Child in Time (winner of the Whitbread Novel of the Year Award, 1987), Amsterdam (winner of the Booker Prize, 1998), Atonement,Sweet Tooth, and The Children Act. He lives in London. Ian McEwan’s Edge Bio Page.
The death cult chose its city well—Paris, secular capital of the world, as hospitable, diverse and charming a metropolis as was ever devised. And the death cult chose its targets in the city with ghoulish, self-damning accuracy—everything they loathed stood plainly before them on a happy Friday evening: men and women in easy association, wine, free-thinking, laughter, tolerance, music—wild and satirical rock and blues. The cultists came armed with savage nihilism and a hatred that lies beyond our understanding. Their protective armour was the suicide belt, their idea of the ultimate hiding place was the virtuous after-life, where the police cannot go. (The jihadist paradise is turning out to be one of humanity’s worst ever ideas; slash and burn in this life, eternal rest among kitsch in the next).
Paris, dazed and subdued, woke this morning to reflect on its new circumstances. Those of us who were out on the town last night can only wonder at the vagaries of chance that lets us live and others die. As the slaughter began, my wife and I were in a venerable Paris institution, a cliché of the modest good life since 1845. In this charming restaurant in the sixieme, one shares crowded tables with good-willed strangers, visitors and locals in a friendly crush. With our Pouilly Fume and filets d’hareng, we were as good a target as any. The cult chose the onzieme, the dixieme, barely a mile away and we didn’t know a thing.
Now we do. What are those changed circumstances? Security will tighten and Paris must become a little less charming. The necessary tension between security and freedom will remain a challenge. The death-cult’s bullets and bombs will come again, here or somewhere else, we can be sure. The citizens of London, New York, Berlin are paying close and nervous attention. In January we were all CharlieHebdo. Now, we are all Parisians and that at least, in a dark time, is a matter of pride.
“Eskimo snow” redirects here. For the album by Why?, see Eskimo Snow.
The claim that Eskimo languages have an unusually large number of words for snow is a widespread idea first voiced by Franz Boas and has become a cliché; it is often used to illustrate the way in which language embodies different local concerns in different parts of the world. In fact, the Eskimo–Aleut languages have about the same number of distinct word roots referring to snow as English does, but the structure of these languages tends to allow more variety as to how those roots can be modified in forming a single word. A good deal of the ongoing debate thus depends on how one defines “word”, and perhaps even “word root”.
The first re-evaluation of the claim was by linguist Laura Martin in 1986, who traced the history of the claim and argued that its prevalence had diverted attention from serious research into linguistic relativity. A subsequent influential and humorous, and polemical, essay by Geoff Pullum repeated Martin’s critique, calling the process by which the so-called “myth” was created the “Great Eskimo Vocabulary Hoax”. Pullum argued that the fact that number of word roots for snow is similar in Eskimoan languages and English proves that there exists no difference in the breadth of their respective vocabularies to define snow. Other specialists in the matter of Eskimoan languages and their knowledge of snow and especially sea ice, refute this notion and defend Boas’ original fieldwork amongst the Inuit of Baffin Island.
Languages in the Inuit and Yupik language groups add suffixes to words to express the same concepts expressed in English and many other languages by means of compound words, phrases, and even entire sentences. One can create a practically unlimited number of new words in the Eskimoan languages on any topic, not just snow, and these same concepts can be expressed in other languages using combinations of words. In general and especially in this case, it is not necessarily meaningful to compare the number of words between languages that create words in different ways due to different grammatical structures.
Opponents of the “Hoax” theory have stated that Boas, who lived among Baffin islanders and learnt their language, did in fact take account of the polysynthetic nature of Inuit language and included “only words representing meaningful distinctions” in his account.
Studies of the Sami languages of Norway, Sweden and Finland, conclude that the languages have anywhere from 180 snow- and ice-related words and as many as 300 different words for types of snow, tracks in snow, and conditions of the use of snow.
The first reference to Inuit having multiple words for snow is in the introduction to Handbook of American Indian languages (1911) by linguist and anthropologistFranz Boas. He says:
To take again the example of English, we find that the idea of WATER is expressed in a great variety of forms: one term serves to express water as a LIQUID; another one, water in the form of a large expanse (LAKE); others, water as running in a large body or in a small body (RIVER and BROOK); still other terms express water in the form of RAIN, DEW, WAVE, and FOAM. It is perfectly conceivable that this variety of ideas, each of which is expressed by a single independent term in English, might be expressed in other languages by derivations from the same term. Another example of the same kind, the words for SNOW in Eskimo, may be given. Here we find one word, aput, expressing SNOW ON THE GROUND; another one, qana, FALLING SNOW; a third one, piqsirpoq, DRIFTING SNOW; and a fourth one, qimuqsuq, A SNOWDRIFT.
The essential morphological question is why a language would say, for example, “lake”, “river”, and “brook” instead of something like “waterplace”, “waterfast”, and “waterslow”. English has more than one snow-related word, but Boas’s intent may have been to connect differences in culture with differences in language.
“We [English speakers] have the same word for falling snow, snow on the ground, snow hard packed like ice, slushy snow, wind-driven snow — whatever the situation may be. To an Eskimo, this all-inclusive word would be almost unthinkable….”
Later writers, prominently Roger Brown in his “Words and things” and Carol Eastman in her “Aspects of Language and Culture”, inflated the figure in sensationalized stories: by 1978, the number quoted had reached fifty, and on February 9, 1984, an unsigned editorial in The New York Times gave the number as one hundred.
There is no one Eskimo language. A number of cultures are referred to as Eskimo, and a number of different languages are termed Eskimo–Aleut languages. These languages may have more or fewer words for “snow”, or perhaps more importantly, more or fewer words that are commonly applied to snow, depending on which language is considered.
Three distinct word roots with the meaning “snow” are reconstructed for the Proto-Eskimo language *qaniɣ ‘falling snow’, *aniɣu ‘fallen snow’, and *apun ‘snow on the ground’. These three stems are found in all Inuit languages and dialects—except for West Greenlandic, which lacks aniɣu. The Alaskan and Siberian Yupik people (among others) however, are not Inuit peoples, nor are their languages Inuit or Inupiaq, but all are classifiable as Eskimos, lending further ambiguity to the “Eskimo Words for Snow” debate.
abGeoffrey K. Pullum’s explanation in Language Log: The list of snow-referring roots to stick [suffixes] on isn’t that long [in the Eskimoan language group]: qani– for a snowflake, apu– for snow considered as stuff lying on the ground and covering things up, a root meaning “slush”, a root meaning “blizzard”, a root meaning “drift”, and a few others — very roughly the same number of roots as in English. Nonetheless, the number of distinct words you can derive from them is not 50, or 150, or 1500, or a million, but simply unbounded. Only stamina sets a limit.
The seven most common English words for snow are snow, hail, sleet, ice, icicle, slush, and snowflake. English also has the related word glacier and the four common skiing terms pack, powder, crud, and crust, so one can say that at least 12 distinct words for snow exist in English.
Igor Krupnik, Ludger Müller-Wille, Franz Boas and Inuktitut Terminology for Ice and Snow: From the Emergence of the Field to the “Great Eskimo Vocabulary Hoax”, SIKU: Knowing Our Ice, Springer Verlag, 2010.
“On ‘Eskimo Words for Snow’: The Life Cycle of a Linguistic Misconception,” by Piotr Cichocki and Marcin Kilarski (Historiographia Linguistica) 37, 2010, Pages 341-377
People who live in an environment in which snow or different kinds of grass, for example, play an important role are more aware of the different characteristics and appearances of different kinds of snow or grass and describe them in more detail than people in other environments. It is however not meaningful to say that people who see snow or grass as often but use another language have less words to describe it if they add the same kind of descriptive information as separate words instead of as “glued-on” (agglutinated) additions to a similar number of words. In other words, English speakers living in Alaska, for example, have no trouble describing as many different kinds of snow as Inuit speakers.
David Robson, New Scientist 2896, December 18 2012, Are there really 50 Eskimo words for snow?, “Yet Igor Krupnik, an anthropologist at the Smithsonian Arctic Studies Center in Washington DC believes that Boas was careful to include only words representing meaningful distinctions. Taking the same care with their own work, Krupnik and others have now charted the vocabulary of about 10 Inuit and Yupik dialects and conclude that there are indeed many more words for snow than in English (SIKU: Knowing Our Ice, 2010). Central Siberian Yupik has 40 such terms, whereas the Inuit dialect spoken in Nunavik, Quebec, has at least 53, including matsaaruti, wet snow that can be used to ice a sleigh’s runners, and pukak, for the crystalline powder snow that looks like salt. For many of these dialects, the vocabulary associated with sea ice is even richer.”
Ole Henrik Magga, Diversity in Saami terminology for reindeer, snow, and ice, International Social Science Journal Volume 58, Issue 187, pages 25–34, March 2006.
^Nils Jernsletten,- “Sami Traditional Terminology: Professional Terms Concerning Salmon, Reindeer and Snow”, Sami Culture in a New Era: The Norwegian Sami Experience. Harald Gaski ed. Karasjok: Davvi Girji, 2997.
Fortescue, Michael, Steven Jacobson, and Lawrence Kaplan. 1993. Comparative Eskimo Dictionary with Aleut Cognates, Fairbanks, Alaska Native Language Center
Kaplan, Lawrence. 2003. Inuit Snow Terms: How Many and What Does It Mean? In: Building Capacity in Arctic Societies: Dynamics and shifting perspectives. Proceedings from the 2nd IPSSAS Seminar. Iqaluit, Nunavut, Canada: May 26-June 6, 2003, ed. by François Trudel. Montreal: CIÉRA — Faculté des sciences sociales Université Laval. http://www.uaf.edu/anlc/snow/
Martin, Laura (1986). “Eskimo Words for Snow: A case study in the genesis and decay of an anthropological example”. American Anthropologist 88 (2), 418-23. 
Pullum, Geoffrey K. (1991). The Great Eskimo Vocabulary Hoax and other Irreverent Essays on the Study of Language. University of Chicago Press. 
Spencer, Andrew (1991). Morphological theory. Blackwell Publishers Inc. p. 38. ISBN0-631-16144-9.
Kaplan, Larry (2003). Inuit Snow Terms: How Many and What Does It Mean?. In: Building Capacity in Arctic Societies: Dynamics and shifting perspectives. Proceedings from the 2nd IPSSAS Seminar. Iqaluit, Nunavut, Canada: May 26-June 6, 2003, ed. by François Trudel. Montreal: CIÉRA—Faculté des sciences sociales Université Laval. 
Cichocki, Piotr and Marcin Kilarski (2010). “On ‘Eskimo Words for Snow’: The life cycle of a linguistic misconception”. Historiographia Linguistica 37 (3), 341–377. 
Krupnik, Igor and Müller-Wille, Ludger (2010). Franz Boas and Inuktitut Terminology for Ice and Snow: From the Emergence of the Field to the “Great Eskimo Vocabulary Hoax”, chapter in SIKU: Knowing Our Ice; Documenting Inuit Sea Ice Knowledge and Use, Springer Verlag, 2010, ISBN 978-90-481-8586-3.
Robson, David (2012). Are there really 50 Eskimo words for snow?, New Scientist no. 2896, 72-73. 
Weyapuk, Winton Jr, et al. (2012). Kiŋikmi Sigum Qanuq Ilitaavut [Wales Inupiaq Sea Ice Dictionary]. Washington DC: Arctic Studies Center Smithsonian.
Eskimos do not have 100’s of words for snow, and other myths debunked
(CBS) – C.G.P. Grey makes us smarter every day. We’ve postedquite a fewvideos of this knowledgeable gentleman with the mellifluous voice. In this latest installment, some common misconceptions are debunked. Some of us are particularly glad to know that cracking knuckles does not in fact cause arthritis and we look forward to bringing this up the next time our mother calls.
As for the rest: hit play, you might just learn something.
This certainly clears quite a few things up. It’s funny how, once you take a moment to think about them, most myths are easily debunked. Of course you can’t see the Great Wall of China from space. Who ever would have thought that?
Credit, as always, goes to that master edutainer C.G.P. Grey. Check out the rest of his YouTube page HERE.
LETTRE DE LONDRES. Au Royaume-Uni aussi, l’ascenseur social est en panne. Seuls 20 % des travailleurs pauvres ont réussi à sortir de la trappe des bas salaires au cours de la dernière décennie. Et les enfants élevés dans des milieux défavorisés ont six fois moins de chances que les filles et fils de bonne famille d’accéder à une université d’élite ouvrant sur les meilleurs emplois. La musique est connue. Mais les inégalités à la sauce british ont une singularité qui vient d’être analysée dans un retentissant rapport : elles se perpétuent par le langage.
On le sait depuis George Bernard Shaw et son Pygmalion (devenu My Fair Lady au cinéma) : la manière de parler est, au Royaume-Uni, un puissant marqueur social. Aujourd’hui encore, s’exprimer avec un accent typique des classes populaires, par exemple de type cockney ou gallois, vous « exclut systématiquement des meilleurs emplois », même à qualification égale, indique l’étude rendue publique, le 15 juin, par la commission sur la mobilité sociale et la pauvreté des enfants.
Tout se passe comme si les entreprises les plus prisées faisaient passer aux candidats à l’emploi un « test de distinction » (« posh test »), explique l’ancien ministre travailliste Alan Milburn, qui préside cette instance rattachée au ministère de l’éducation. Ne pas parler anglais avec l’accent chic très reconnaissable d’Oxbridge (contraction d’Oxford et Cambridge) comme les membres de l’élite économique et politique…
One Enlightenment aspiration that the science-fiction industry has long taken for granted, as a necessary intergalactic conceit, is the universal translator. In a 1967 episode of “Star Trek,” Mr. Spock assembles such a device from spare parts lying around the ship. An elongated chrome cylinder with blinking red-and-green indicator lights, it resembles a retracted light saber; Captain Kirk explains how it works with an off-the-cuff disquisition on the principles of Chomsky’s “universal grammar,” and they walk outside to the desert-island planet of Gamma Canaris N, where they’re being held hostage by an alien. The alien, whom they call The Companion, materializes as a fraction of sparkling cloud. It looks like an orange Christmas tree made of vaporized mortadella. Kirk grips the translator and addresses their kidnapper in a slow, patronizing, put-down-the-gun tone. The all-powerful Companion is astonished.
The exchange emphasizes the utopian ambition that has long motivated universal translation. The Companion might be an ion fog with coruscating globules of viscera, a cluster of chunky meat-parts suspended in aspic, but once Kirk has established communication, the first thing he does is teach her to understand love. It is a dream that harks back to Genesis, of a common tongue that perfectly maps thought to world. In Scripture, this allowed for a humanity so well coordinated, so alike in its understanding, that all the world’s subcontractors could agree on a time to build a tower to the heavens. Since Babel, though, even the smallest construction projects are plagued by terrible delays.
Translation is possible, and yet we are still bedeviled by conflict. This fallen state of affairs is often attributed to the translators, who must not be doing a properly faithful job. The most succinct expression of this suspicion is “traduttore, traditore,” a common Italian saying that’s really an argument masked as a proverb. It means, literally, “translator, traitor,” but even though that is semantically on target, it doesn’t match the syllabic harmoniousness of the original, and thus proves the impossibility it asserts.
Translation promises unity but entails betrayal. In his wonderful survey of the history and practice of translation, “Is That a Fish in Your Ear?” the translator David Bellos explains that the very idea of “infidelity” has roots in the Ottoman Empire. The sultans and the members of their court refused to learn the languages of the infidels, so the task of expediting communication with Europe devolved upon a hereditary caste of translators, the Phanariots. They were Greeks with Venetian citizenship residing in Istanbul. European diplomats never liked working with them, because their loyalty was not to the intent of the foreign original but to the sultan’s preference. (Ottoman Turkish apparently had no idiom about not killing the messenger, so their work was a matter of life or death.) We retain this lingering association of translation with treachery.
Google Translate is far and away the venture that has done the most to realize the old science-fiction dream of serene, unrippled exchange. The search giant has made ubiquitous those little buttons, in email and on websites, that deliver instantaneous conversion between language pairs. Google says the service is used more than a billion times a day worldwide, by more than 500 million people a month. Its mobile app ushers those buttons into the physical world: The camera performs real-time augmented-reality translation of signs or menus in seven languages, and the conversation mode allows for fluent colloquy, mediated by robot voice, in 32. There are stories of a Congolese woman giving birth in an Irish ambulance with the help of Google Translate and adoptive parents in Mississippi raising a child from rural China.
Since 2009, the White House’s policy paper on innovation has included, in its list of near-term priorities, “automatic, highly accurate and real-time translation” to dismantle all barriers to international commerce and cooperation. If that were possible, a variety of local industries would lose the final advantage of their natural camouflage, and centralization — in social networking, the news, science — would accelerate geometrically. Nobody in machine translation thinks that we are anywhere close to that goal; for now, efforts in the discipline are mostly concerned with the dutiful assembly of “cargo trucks” to ferry information across linguistic borders. The hope is that machines might efficiently and cheaply perform the labor of rendering sentences whose informational content is paramount: “This metal is hot,” “My mother is in that collapsed house,” “Stay away from that snake.” Beyond its use in Google Translate, machine translation has been most successfully and widely implemented in the propagation of continent-spanning weather reports or the reproduction in 27 languages of user manuals for appliances. As one researcher told me, “We’re great if you’re Estonian and your toaster is broken.”
Warren Weaver, a founder of the discipline, conceded: “No reasonable person thinks that a machine translation can ever achieve elegance and style. Pushkin need not shudder.” The whole enterprise introduces itself in such tones of lab-coat modesty. The less modest assumption behind the aim, though, is that it’s possible to separate the informational content of a sentence from its style. Human translators, like poets, might be described as people for whom such a distinction is never clear or obvious. But human translators, today, have virtually nothing to do with the work being done in machine translation. A majority of the leading figures in machine translation have little to no background in linguistics, much less in foreign languages or literatures. Instead, virtually all of them are computer scientists. Their relationship with language is mediated via arm’s-length protective gloves through plate-glass walls.
Many of the algorithms used by Google and Skype Translator have been developed and honed by university researchers. In May, a computational linguist named Lane Schwartz, who teaches at the University of Illinois at Urbana-Champaign, hosted the first Machine Translation Marathon in the Americas, a weeklong hackathon to improve the open-source tools that those without Google resources share. Urbana-Champaign is largely known outside Illinois for two people: David Foster Wallace, who grew up there, and Marc Andreessen, who invented the first widely adopted graphical web browser as a student at the university. (Schwartz suggested a third: HAL 9000.) It is tempting to see them as the two ends of a spectrum: Wallace as a partisan of neologism, allusion and depth, Andreessen on the side of proliferation, access and breadth.
At this conference, at least, the spirit of Andreessen prevailed. Though attendees hailed from places like Greece, India, Israel, Suriname and Taiwan, almost nobody betrayed any interest in language as such. They understood that language is a rich and slippery thing, but they were there for the math.
The marathon took place at a conference center attached to something called an iHotel. The center was a U-shaped hallway lined by rooms named after virtues — the Leadership Boardroom, the Loyalty Room, the Knowledge Room, the Innovation Room and the Excellence Room. At the presentations, computer scientists with straight faces regularly made comments like “Paragraphs arguably should be coherent in topic” or “Grammatical structure can matter in a sentence.” One presenter said that sometimes French places its adjective before the noun and sometimes after, but that, he concluded with a short shrug, “nobody knows why or when.”
One of the American marathon presenters wore two consecutive days of threadbare grammar T-shirts — one read, “Good grammar costs nothing!” and the other, “I am silently correcting your grammar” — so I imagined he might see his algorithmic work in the context of broader linguistic interests. I asked him if he spoke any other languages, and he said: “I speak American high-school French, which is to say I don’t. But it’s surprising how little it helps to know another language. When you’re working with so many languages, it’s actually not helpful to know one.” (His third T-shirt read, “Don’t follow me, I’m lost, too.”)
The possibility of machine translation, Schwartz explained, emerged from World War II. Weaver, an American scientist and government administrator, had learned about the work of the British cryptographers who broke the Germans’ Enigma code. It occurred to him that cryptographic investigations might solve an immediate postwar problem: keeping abreast of Russian scientific publications. There simply weren’t enough translators around, and even if there were, it would require an army of them to stay current with the literature. “When I look at an article in Russian,” Weaver wrote, “I say: ‘This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.’ ” In this view, Russian was merely English in frilly Cyrillic costume, only one small step removed from pig Latin.
Within a year or two, this idea was understood as absurd, and yet the broader notion of algorithmic processing held. By 1954 the American public was treated to a demonstration of the first nonnumerical application of computing. A secretary typed a Russian sentence onto a series of punch cards; the computer whirred and spat out an English equivalent. The Christian Science Monitor wrote that the “electronic brain” at the demonstration “didn’t even strain its superlative versatility and flicked out its interpretation with a nonchalant attitude of assumed intellectual achievement.”
That demonstration, however, was basically rigged. The computer had been given a pidgin vocabulary (a total of 250 words) and fed a diet of simple declarative sentences. In 1960, one of the earliest researchers in the field, the philosopher and mathematician Yehoshua Bar-Hillel, wrote that no machine translation would ever pass muster without human “post-editing”; he called attention to sentences like “The pen is in the box” and “The box is in the pen.” For a translation machine to be successful in such a situation of semantic ambiguity, it would need at hand not only a dictionary but also a “universal encyclopedia.” The brightest future for machine translation, he suggested, would rely on coordinated efforts between plodding machines and well-trained humans. The scientific community largely came to accept this view: Machine translation required the help of trained linguists, who would derive increasingly abstract grammatical rules to distill natural languages down to the sets of formal symbols that machines could manipulate.
This paradigm prevailed until 1988, year zero for modern machine translation, when a team of IBM’s speech-recognition researchers presented a new approach. What these computer scientists proposed was that Warren Weaver’s insight about cryptography was essentially correct — but that the computers of the time weren’t nearly powerful enough to do the job. “Our approach,” they wrote, “eschews the use of an intermediate mechanism (language) that would encode the ‘meaning’ of the source text.” All you had to do was load reams of parallel text through a machine and compute the statistical likelihood of matches across languages. If you train a computer on enough material, it will come to understand that 99.9 percent of the time, “the butterfly” in an English text corresponds to “le papillon” in a parallel French one. One researcher quipped that his system performed incrementally better each time he fired a linguist. Human collaborators, preoccupied with shades of “meaning,” could henceforth be edited out entirely.
Though some researchers still endeavor to train their computers to translate Dante with panache, the brute-force method seems likely to remain ascendant. This statistical strategy, which supports Google Translate and Skype Translator and any other contemporary system, has undergone nearly three decades of steady refinement. The problems of semantic ambiguity have been lessened — by paying pretty much no attention whatsoever to semantics. The English word “bank,” to use one frequent example, can mean either “financial institution” or “side of a river,” but these are two distinct words in French. When should it be translated as “banque,” when as “rive”? A probabilistic model will have the computer examine a few of the other words nearby. If your sentence elsewhere contains the words “money” or “robbery,” the proper translation is probably “banque.” (This doesn’t work in every instance, of course — a machine might still have a hard time with the relatively simple sentence “A Parisian has to have a lot of money to live on the Left Bank.”) Furthermore, if you have a good probabilistic model of what standard sentences in a language do and don’t look like, you know that the French equivalent of “The box is in the ink-filled writing implement” is encountered approximately never.
Contemporary emphasis is thus not on finding better ways to reflect the wealth or intricacy of the source language but on using language models to smooth over garbled output. A good metaphor for the act of translation is akin to the attempt to answer the question “What player in basketball corresponds to the quarterback?” Current researchers believe that you don’t really need to know much about football to answer this question; you just need to make sure that the people who have been drafted to play basketball understand the game’s rules. In other words, knowledge of any given source language — and the universal cultural encyclopedia casually encoded within it — is growing ever more irrelevant.
Many computational linguists continue to claim that, after all, they are interested only in “the gist” and that their duty is to find inexpensive and fast ways of trucking the gist across languages. But they have effectively arrogated to themselves the power to draw a bright line where “the gist” ends and “style” begins. Human translators think it’s not so simple. The machinist’s attitude is that when someone’s mother is trapped under a house, it’s fussy and self-important to worry too much about nuance. They see the redundancy and allusiveness of natural languages as a matter not of intricacy but of confusion and inefficiency. Most valuable utterances revert to the mean of statistical probability. If this makes them unpopular with poets and fanciers of language, so be it. “Go to the American Translators Association convention,” one marathon attendee told me, “and you’ll see — they hate us.”
This is to some extent true. As the translator Susan Bernofsky put it to me, “They create the impression that translation is not an art.” (A widely admired literary translator, who wished to remain anonymous, admitted that although she worries about machine translation’s mission creep, she thinks Google Translate is a wonderful tool for writing notes to the woman who cleans her house.)
What mostly annoys human translators isn’t the arrogance of machines but their appropriation of the work of forgotten or anonymous humans. Machine translation necessarily supervenes on previous human effort; otherwise there wouldn’t be the parallel corpora that the machines need to do their work. I mentioned to an Israeli graduate student that I had been reading the Wikipedia page of Yehoshua Bar-Hillel and had found out that his granddaughter, Gili, is a minor celebrity in Israel as the translator of the “Harry Potter” books. He hadn’t heard of her and didn’t seem interested in the process by which a publisher paid to import books about magic for children. But we would have no such tools as Google Translate for the Hebrew-English language pair if Bar-Hillel had not hand-translated, with care, more than 4,000 pages of an extremely useful parallel corpus. In a sense, their machines aren’t actually translating; they’re just speeding along tracks set down by others. This is the original sin of machine translation: The field would be nowhere without the human translators they seek, however modestly, to supersede.
Perhaps to paper over the associated guilt, the group in Urbana-Champaign cultivated a minor resentment toward their human counterparts. More than once I heard someone at the marathon refer to the fact that human translators are finicky and inconsistent and prone to complaint. Quality control is impossible. As one attendee explained to me, “If you show a translator an unidentified version of his own translation of a text from a year ago, he’ll look it over and tell you it’s terrible.”
One computational linguist said, with a knowing leer, that there is a reason we have more than 20 translations in English of “Don Quixote.” It must be because nobody ever gets it right. If the translators can’t even make up their own minds about what it means to be “faithful” or “accurate,” what’s the point of worrying too much about it? Let’s just get rid of the whole antiquated fidelity concept. All the Sancho Panzas, all the human translators and all the computational linguists are in the same leaky boat, but the machinists are bailing out the water while the humans embroider monograms on the sails.
But like many engineers, the computational linguists are so committed to the power and craftsmanship of their means that they tend to lose perspective on whose ends they are advancing. The problem with human translators, from the time of the Phanariots, is that there is always the possibility that they might be serving the ends of their bosses rather than the intent of the text itself. But at least a human translator asks the very questions — What purpose is this text designed to serve? What aims are encoded in this language? — that a machine regards as entirely beside the point.
The problem is that all texts have some purpose in mind, and what a good human translator does is pay attention to how the means serve the end — how the “style” exists in relationship to “the gist.” The oddity is that belief in the existence of an isolated “gist” often obscures the interests at the heart of translation. Toward the end of the marathon, I asked a participant why he chose to put his computer-science background to the service of translation. He mentioned, as many of them did, a desire to develop tools that would be helpful in earthquakes or war. Beyond that, he said, he hoped to help ameliorate the time lag in the proliferation of international news. I asked him what he meant.
“There was, for example, a huge delay with the Germanwings crash.”
It wasn’t the example I was expecting. “But what was that delay, like 10 or 15 minutes?”
He cocked his head. “That’s a huge delay if you’re a trader.”
I didn’t say anything informational in words, but my body or face must have communicated a response the engineer mistranslated as ignorance. “It’s called cross-lingual arbitrage. If there’s a mine collapse in Spanish, you want to make a trade as quickly as possible.”
” I am a writer and translator, and have told myself stories for as long as I can remember.
Raised in Newark and Bradford, I now live in mid-Wales with my husband and two teenage children.
I studied Anglo-Saxon, Norse and Celtic at Cambridge University, and after a brief spell as a taxi driver worked for several years as a chartered surveyor before returning to my first love – languages. I translate from German, French and Welsh into English, and have been teaching myself Croatian while researching for my debut novel, Someone Else’s Conflict. ”
A former chartered surveyor, independent translator since 1997, Alison Layland translates from French into English, German and Welsh. Readers in the United Kingdom discovered Yanick Lahens thanks to her.
Alison Layland was born in England and now lives in Wales, but France is her second home. ‘French was the first language I learned at school. It’s a language that plays an important part in my life, I am passionately interested in the French language and literature,’ she tells us.After studying ancient languages at university – Anglo-Saxon, Norse and Celtic – and modern – French and German – the future translator’s career took an unexpected turn. ‘Chance events led me to work as a chartered surveyor for eight years, before following my instincts to become a translator at the end of the 90s. I think that experience was very useful to me in several ways, in as much as an author or a translator can take advantage of the most varied experience in all domains; it is even an essential part of their training.’
When she finally got started, Alison Layland began by translating creative commercial documents (brochures,Web articles) before tackling books, travelogues and history, from French and German into English. ‘Yet my dream was always to translate novels, especially because I am an author of fiction myself,’she confides. Her dream became reality in 2010 when she entered a competition organised by the Wales Literature Exchange, an organisation which promotes the translation of Welsh and international literature in collaboration with Oxfam. ‘That year, a new Haitian writer was chosen to increase public awareness of the problems in Haiti, devastated bythe tragic earthquake. Working on her text for the competition, I discoveredthe country and became interested in its literature, culture and history,’ sherecalls. At the end of the competition – which she won – Welsh publisher Seren commissioned a translation of La Couleur de l’Aube (SabineWespieser) by Haitian author Yanick Lahens. A success that opened the door to literary publishing and led to new horizons, particularly with small independent publishers in the UK who specialize in foreign literature.
J’ai rencontré Catherine au Café des Freelances, mais je connaissais déjà son nom auparavant, car elle est très présente sur les réseaux sociaux !
Prénom, métier, âge et parcours en une phrase Je m’appelle Catherine, je suis traductrice anglophone et j’ai 38 ans. J’habite en France depuis 1998 et je traduis du français vers l’anglais.
Depuis quand es-tu indépendant(e)/as-tu créé ta société Je travaille à mon compte depuis octobre 2009.
Pour quelle(s) raison(s) as-tu choisi d’être indépendante ? La patronne, c’est moi ! J’aime gérer ma carrière et fixer mes horaires. Je suis à la fois traductrice, blogueuse, comptable, secrétaire, community manager et chef d’entreprise.
Quel statut as-tu choisi ? Pourquoi ? Pour l’instant je suis auto-entrepreneur pour la facilité des démarches. Mais en 2012 je changerai très probablement de statut.
C’est quoi, ta journée-type , si ça existe ? Un peu de Twitter, Facebook et emails, et ensuite, quelques heures de traduction. Et aussi des rendez-vous chez le client et des rencontres enrichissantes avec d’autres traducteurs – mes relations offline sont capitales.
Si tu te projettes dans 10 ans, tu imagines quoi ? Je serai toujours en apprentissage parce que même après plusieurs années d’expérience, il faut se former. Le métier du traducteur évolue et je compte rester au courant.
Quel conseil donnerais-tu à quelqu’un qui voudrait se lancer ? Rencontrer d’autres travailleurs indépendants du même métier pour échanger des conseils et partager des connaissances.
As-tu un site, un blog, un profil Viadeo, un mail qui permettrait d’entrer en contact avec toi ? Bien sûr ! Grâce à ma présence sur le Web, je reste en contact avec d’autres traducteurs et avec les clients et prospects. Mon blog sur la traduction s’appelle Catherine Translates . Si vous comprenez l’anglais, vous verrez ma façon de travailler, mes idées sur le métier de la traduction et des astuces pour d’autres traducteurs freelances. J’ai une toute nouvelle page Facebook . Mon site Internet est à : http://www.translate-traduire.com. Je suis sur LinkedIn , et vous pouvez me contacter à firstname.lastname@example.org.