Opinion & features
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In this episode we talk about a new tool for freelance translators. I am all for efficiency and organization, but I am also quite lazy, and have struggled keeping track of my projects, number of words and how valuable different projects have been for me, how long they took etc. But now there is a tool that is super easy to use, that does all this for me, and much more. I interview the co-founder and co-creator of the tool Caroline Bries.
Important things mentioned in this episode:
- LSP.expert as a project management tool for freelance translators
- All the functions in LSP.expert – quoting, job tracking, expenses, income, reports, invoicing, outsourcing, timer and much more
- Security and support for LSP.expert
Useful links mentioned in this episode:
- Review of LSP.Expert by Silver Tongue Translations
- LSP.Experts Facebook page
- How LSP.Expert revolutionized my business – Review on The Open Mic
Listen to the interview >>
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I just got notified, that PayPal is changing its Tems & Conditions on May 25th 2018.
You should give it 5 minutes and check the conditions concerning your country of residence and also which countries are the ones, you get the most transactions from. Mind, that there’s a huge difference if payments are based on bank accounts or credit cards.
I e.g. just realized, that it makes a big financially difference, that my account is registered in Germany, not Austria, and have to make the necessary changes soon.
How do you handle your PayPal fees anyway? Are you simply accepting that loss of money or do you add the costs in your invoices, so the client has to pay them?
Have a great and productive week,
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Business is looking good in the language sector. CSA Research’s business confidence survey of the CEOs of the largest language service providers found 2017 to be a growth year, and respondents optimistically entered 2018. Sector revenue and language output continue to rise as the content and code that power economies are becoming more global. Our annual survey will give a more complete picture of the market as we collect and analyze the data.
This optimism plays out against a backdrop of concerns about the future of language services, on both the demand and supply side of the market. Buyers worry about the need to process ever-growing content volumes into more language pairs – but with relatively stable budgets. Meanwhile, they must deal with their management’s expectations that Amazon and Google Translate will take care of that pesky language problem once and for all – and with less complexity and at a lower cost.
On the supply side, LSPs express fundamental anxiety about the sustainability of their business models. We hear concerns across all tiers of the language service market:
- Automation and procurement specialists marginalize small providers. These LSPs wonder where they fit in the market and how they’ll survive. They worry about sales, staffing, and the need for more – and competition with – increasingly powerful technology. Will their translation work be replaced by a bunch of Amazon servers? Will their project management value be replaced by bots? Further, they must also contend with commoditizing forces beyond their control such as distant procurement functions created when their clients are acquired by global behemoths.
- Market forces squeeze mid-sized companies from both ends. Further up the value chain, medium-sized companies face niche specialists on one side, generalist multi-language vendors on the other, and those same procurement challenges. These mid-tier firms strategize about how they can scale up more quickly and compete against the economies of scale that the largest LSPs bring to bear. They plan growth both organically and by strategic liaisons or acquisitions.
- The largest providers scramble for scale. At the top of the pyramid, the largest LSPs position themselves to get even bigger, scale to their clients’ fondest dreams for global content, strive for organic growth, and engage in pitched battles to buy a shrinking pool of mid-sized acquisitions. They’re also investing more in building their own technology as they climb to the higher stages of LSP Metrix maturity.
In conversation after conversation, we witness debates about technology. On one side, advocates pitch the importance of advanced technology to growth and scalability. On the other, naysayers anxiously await the swarms of AI-driven language bots and machine learning that will surely exterminate their companies. Their concern extends beyond machine translation to project management − will simple rule-based automation and deep machine learning conspire to eliminate the last humans on the language shop floor? CSA Research characterizes this angst as techno-phobia − or maybe more precisely, phobAI or its homophone FOBAI, the fear of being AI’d out of existence.
But we contend that FOBAI is a symptom of a bigger problem – mistaken identity. LSPs concerned about having automation wipe them out think they’re in the translation business. They’re not. Language service providers are business process outsourcers (BPOs), traditionally tasked with the job of rendering one language into another but now on the cusp of a much broader role managing more of the content assets for their digitizing clients. While conventional agencies still quibble about how many pennies they charge to translate a word, their buyers are far more interested in bigger content issues tied to their digital transformation. These clients are bringing all their information online, optimizing it, adapting and adopting technology to manage it, and making those assets available across the enterprise. This digitization has forced them to re-think internal systems and processes, pry open databases and content management systems, and educate their staff and suppliers in this new model.
How will their clients’ digitization strategies transform the LSPs supplying them?
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We’re in a localization and globalization market now where more words are translated every day through machine translation than what was translated in the entire human language corpus in the past.
Not only does such a massive amount of machine translation radically change the role of human translators, it also creates a whole new range of issues that impact the translation and globalization paradigm itself.
And one of the most important issues is ethics.
In an era when entire translations or at least substantial parts of them are often done by machine instead of by professional translators, what does it mean to provide “services” from an ethical perspective as far as translators and LSPs are concerned?
In this week’s episode of Globally Speaking, our hosts Renato Beninatto and M.W. Stevens discuss this very important issue that affects everyone involved in the language industry—both providers and buyers of translation services alike.
Major topics include:
- What needs to be disclosed to buyers and what doesn’t?
- Are language professionals now selling a product or a service?
- When are translators in breach of a client contract by using machine translation, and when are they not?
- Why machine translation is unlikely to ever replace the need for professionally trained translators.
- How do LSPs charge for projects in which machine translation plays a major role?
Listen to the podcast >>
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Jack Welde helps companies make more money by speaking their customers’ language — literally. Welde is the Co-Founder and CEO of Smartling, a disruptive translation services company that uses a combination of human and machine translation to help companies enter new markets faster.
Welde says in the interview that consumers are 75 percent more likely to convert when they are being sold to in their native language — even if they are comfortable with the language they’re reading. Smartling measures the accuracy of translations with data, as well as the translations’ effectiveness in reaching new customers.
Read the interview or listen to the podcast >>
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In this episode we are talking about what methods work to find translation clients in 2018. With me I have a translation company owner and translator, Sherif Abuzid, who is sharing his best tips for finding clients. These are suggestions based on his experience. Pick the ones that work for your situation, depending on experience and preference, but also depending on your location.
Important things mentioned in this episode:
- Change in how we find and contact clients during these last 10 years
- What resources for finding clients we should focus on
- How we should contact new clients
- Differences in marketing if you are a newer translator vs a more experienced one
Read more and download/listen to the podcast >>
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By Vijayalaxmi Hegde
By and large, those of us in the localization industry are multi-cultural and multilingual, traveling the world, celebrating the diversity and power of language. We are interested in languages, culture, and language technology, and how they influence and shape our lives. Many of us also cross over from linguistics and language to the technical side of things.
Jost Zetzsche, a localization professional with nearly 20 years of experience, is a thought leader in these areas. He put his expertise into his latest book, Translation Matters. Comprised of 81 essays collected over the past 15 years, Jost’s book describes a world of translation where technology changes rapidly, but where the translator remains the central figure, ever-savvier in using the tools of the trade.
I had the chance to sit down with him and talk about these ideas.
Viju: One very hot topic on the minds of translators today is Machine Translation (MT).
Jost: In part, my book deals with the identity of translators at a time when MT has clearly become important. It’s critical as translators to define who we are and, on the basis of that self-perception, understand our role in the world and our role in relation to things like machine translation. That’s what the book is about to a certain extent.
The way we translators usually approach MT is either we reject it and say, “I don’t want to deal with machine translation,” or we say, “Okay, then I guess I have to do what I’m told to do with machine translation.” And that’s typically post-editing. And while in some situations, post-editing is the right choice, more often than not there are better ways of dealing with machine translation than to not use it at all or only post-edit. For example, translators can greatly benefit from data that is being suggested by machine translation engines without actually “post-editing” complete segments.
Viju: When I think about MT, I also think about another industry hot topic: AI. What are your thoughts on that?
Jost: I just read an article that talked about artificial intelligence, and it said that translators are kind of the canary in the coal mine. If translators become extinct, then we have truly reached a point of no return, where everything has completely changed, where everything has been turned upside down, and where artificial intelligence has essentially taken over.
Translators have a very secure job for a long time to come. If their job becomes insecure, if artificial intelligence, machine translation, is truly able to take over from translators, society will have changed so much that we will not recognize it anymore. Then, essentially, we’re all out of a job. We’ll have to redefine what it means to be productive, what it means to be a human being, what it means to work in the industry, etc.
At that time, there will be what Ray Kurzweil and others are calling this moment of singularity where we don’t need to work in the kinds of professions that we work in today. But, I think translators will be among the last to go.
What translators do is very close to what it means to be human: to be able to communicate in a complex and multi-layered manner. That’s something that computers can’t do unless we reach that point of singularity.
But don’t misunderstand me—it’s not as if we’re not impacted. But our job is NOT being taken away by it.
Viju: What happens to a translator who’s not inclined to learn about the whole range of tools available? Is there space for such a translator anymore?
Jost: Absolutely. You don’t have to be a tech geek to be a translator. My background is the opposite of technical. But I find joy in making technology work for me in a way that is productive and thinking of new ways of making it even more productive. And I think that’s something that translators should have. So that’s not geeky—it’s just looking at what’s out there and finding the right tools that work for you. I don’t think translators need to know all the tools. They just have to have an idea of what is out there and then make intelligent decisions on which tools to use.
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One Sunday, at one of our weekly salsa sessions, my friend Frank brought along a Danish guest. I knew Frank spoke Danish well, since his mother was Danish, and he, as a child, had lived in Denmark. As for his friend, her English was fluent, as is standard for Scandinavians. However, to my surprise, during the evening’s chitchat it emerged that the two friends habitually exchanged emails using Google Translate. Frank would write a message in English, then run it through Google Translate to produce a new text in Danish; conversely, she would write a message in Danish, then let Google Translate anglicize it. How odd! Why would two intelligent people, each of whom spoke the other’s language well, do this? My own experiences with machine-translation software had always led me to be highly skeptical about it. But my skepticism was clearly not shared by these two. Indeed, many thoughtful people are quite enamored of translation programs, finding little to criticize in them. This baffles me.
As a language lover and an impassioned translator, as a cognitive scientist and a lifelong admirer of the human mind’s subtlety, I have followed the attempts to mechanize translation for decades. When I first got interested in the subject, in the mid-1970s, I ran across a letter written in 1947 by the mathematician Warren Weaver, an early machine-translation advocate, to Norbert Wiener, a key figure in cybernetics, in which Weaver made this curious claim, today quite famous:
When I look at an article in Russian, I say, “This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.”
Some years later he offered a different viewpoint: “No reasonable person thinks that a machine translation can ever achieve elegance and style. Pushkin need not shudder.” Whew! Having devoted one unforgettably intense year of my life to translating Alexander Pushkin’s sparkling novel in verse Eugene Onegin into my native tongue (that is, having radically reworked that great Russian work into an English-language novel in verse), I find this remark of Weaver’s far more congenial than his earlier remark, which reveals a strangely simplistic view of language. Nonetheless, his 1947 view of translation-as-decoding became a credo that has long driven the field of machine translation.
Since those days, “translation engines” have gradually improved, and recently the use of so-called “deep neural nets” has even suggested to some observers (see “The Great AI Awakening” by Gideon Lewis-Kraus in The New York Times Magazine, and “Machine Translation: Beyond Babel” by Lane Greene in The Economist) that human translators may be an endangered species. In this scenario, human translators would become, within a few years, mere quality controllers and glitch fixers, rather than producers of fresh new text.
Such a development would cause a soul-shattering upheaval in my mental life. Although I fully understand the fascination of trying to get machines to translate well, I am not in the least eager to see human translators replaced by inanimate machines. Indeed, the idea frightens and revolts me. To my mind, translation is an incredibly subtle art that draws constantly on one’s many years of experience in life, and on one’s creative imagination. If, some “fine” day, human translators were to become relics of the past, my respect for the human mind would be profoundly shaken, and the shock would leave me reeling with terrible confusion and immense, permanent sadness.
Each time I read an article claiming that the guild of human translators will soon be forced to bow down before the terrible swift sword of some new technology, I feel the need to check the claims out myself, partly out of a sense of terror that this nightmare just might be around the corner, more hopefully out of a desire to reassure myself that it’s not just around the corner, and finally, out of my longstanding belief that it’s important to combat exaggerated claims about artificial intelligence. And so, after reading about how the old idea of artificial neural networks, recently adopted by a branch of Google called Google Brain, and now enhanced by “deep learning,” has resulted in a new kind of software that has allegedly revolutionized machine translation, I decided I had to check out the latest incarnation of Google Translate. Was it a game changer, as Deep Blue and AlphaGo were for the venerable games of chess and Go?
I learned that although the older version of Google Translate can handle a very large repertoire of languages, its new deep-learning incarnation at the time worked for just nine languages. (It’s now expanded to 96.)* Accordingly, I limited my explorations to English, French, German, and Chinese.
Before showing my findings, though, I should point out that an ambiguity in the adjective “deep” is being exploited here. When one hears that Google bought a company called DeepMind whose products have “deep neural networks” enhanced by “deep learning,” one cannot help taking the word “deep” to mean “profound,” and thus “powerful,” “insightful,” “wise.” And yet, the meaning of “deep” in this context comes simply from the fact that these neural networks have more layers (12, say) than do older networks, which might have only two or three. But does that sort of depth imply that whatever such a network does must be profound? Hardly. This is verbal spinmeistery.
I am very wary of Google Translate, especially given all the hype surrounding it. But despite my distaste, I recognize some astonishing facts about this bête noire of mine. It is accessible for free to anyone on earth, and will convert text in any of roughly 100 languages into text in any of the others. That is humbling. If I am proud to call myself “pi-lingual” (meaning the sum of all my fractional languages is a bit over 3, which is my lighthearted way of answering the question “How many languages do you speak?”), then how much prouder should Google Translate be, since it could call itself “bai-lingual” (“bai” being Mandarin for 100). To a mere pilingual, bailingualism is most impressive. Moreover, if I copy and paste a page of text in Language A into Google Translate, only moments will elapse before I get back a page filled with words in Language B. And this is happening all the time on screens all over the planet, in dozens of languages.
The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to “decode”—to get away without worrying about what understanding and meaning are. Could it in fact be that understanding isn’t needed in order to translate well? Could an entity, human or machine, do high-quality translation without paying attention to what language is all about? To shed some light on this question, I turn now to the experiments I made.
I began my explorations very humbly, using the following short remark, which, in a human mind, evokes a clear scenario:
In their house, everything comes in pairs. There’s his car and her car, his towels and her towels, and his library and hers.
The translation challenge seems straightforward, but in French (and other Romance languages), the words for “his” and “her” don’t agree in gender with the possessor, but with the item possessed. So here’s what Google Translate gave me:
Dans leur maison, tout vient en paires. Il y a sa voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et les siennes.
The program fell into my trap, not realizing, as any human reader would, that I was describing a couple, stressing that for each item he had, she had a similar one. For example, the deep-learning engine used the word “sa” for both “his car” and “her car,” so you can’t tell anything about either car-owner’s gender. Likewise, it used the genderless plural “ses” both for “his towels” and “her towels,” and in the last case of the two libraries, his and hers, it got thrown by the final “s” in “hers” and somehow decided that that “s” represented a plural (“les siennes”). Google Translate’s French sentence missed the whole point.
Next I translated the challenge phrase into French myself, in a way that did preserve the intended meaning. Here’s my French version:
Chez eux, ils ont tout en double. Il y a sa voiture à elle et sa voiture à lui, ses serviettes à elle et ses serviettes à lui, sa bibliothèque à elle et sa bibliothèque à lui.
The phrase “sa voiture à elle” spells out the idea “her car,” and similarly, “sa voiture à lui” can only be heard as meaning “his car.” At this point, I figured it would be trivial for Google Translate to carry my French translation back into English and get the English right on the money, but I was dead wrong. Here’s what it gave me:
At home, they have everything in double. There is his own car and his own car, his own towels and his own towels, his own library and his own library.
What?! Even with the input sentence screaming out the owners’ genders as loudly as possible, the translating machine ignored the screams and made everything masculine. Why did it throw the sentence’s most crucial information away?
We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered “his” and “hers.” Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s familiar solely with strings composed of words composed of letters. It’s all about ultrarapid processing of pieces of text, not about thinking or imagining or remembering or understanding. It doesn’t even know that words stand for things. Let me hasten to say that a computer program certainly could, in principle, know what language is for, and could have ideas and memories and experiences, and could put them to use, but that’s not what Google Translate was designed to do. Such an ambition wasn’t even on its designers’ radar screens.
Well, I chuckled at these poor shows, relieved to see that we aren’t, after all, so close to replacing human translators by automata. But I still felt I should check the engine out more closely. After all, one swallow does not thirst quench.
Indeed, what about this freshly coined phrase “One swallow does not thirst quench” (alluding, of course, to “One swallow does not a summer make”)? I couldn’t resist trying it out; here’s what Google Translate flipped back at me: “Une hirondelle n’aspire pas la soif.” This is a grammatical French sentence, but it’s pretty hard to fathom. First it names a certain bird (“une hirondelle”—a swallow), then it says this bird is not inhaling or not sucking (“n’aspire pas”), and finally reveals that the neither-inhaled-nor-sucked item is thirst (“la soif”). Clearly Google Translate didn’t catch my meaning; it merely came out with a heap of bull. “Il sortait simplement avec un tas de taureau.” “He just went out with a pile of bulls.” “Il vient de sortir avec un tas de taureaux.” Please pardon my French—or rather, Google Translate’s pseudo-French.
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by Silvio Picinini
We translators may be on the verge of dealing with more math surrounding our beloved words. Until now, the number that mattered most has been word count, because that is how we are paid. And fuzzy matches, of course—that magic algorithm that calculates how much partial help (and payment) we get.
We consider a segment short, medium, or long based on its word count. However, machine translation (MT) offers different ideas about different lengths; even neural MT with all the widespread praise for fluency.
For segments of one or two words, there is little to no fluency. So is that where neural MT goes out the window? Maybe, or maybe not. We have to find out. Either way, all of sudden, segment word counts have become metrics indicative of whether MT will translate fluently. Food for thought: how many words is still short? What is medium? What is long?
Another number to consider is edit distance. It may be coming to a translation productivity tool near you. That is because we will increasingly want to know how much MT is helping us do our job, and the answer to that (or a part of the answer) is how many changes we make to the suggestions we receive.
If the MT is really awesome in a 30-word sentence, we smile, make two changes, and the edit distance is low. If the MT requires reordering the words and fixing brands that were inaccurately translated, then we are putting a lot more effort and the edit distance will reflect that.
As much as I love edit distance, it is just a comparison between one initial “image” of the sentence and the final “image” that we created. But then there is adaptive technology, that keeps changing the suggestions as we move. Working with adaptive technology, there isn’t really an initial “image” or “initial full sentence translation.” So, it is complicated.
Let’s explore some other numbers: what is the average number of words per sentence in our text?
If it is high—let’s say 16—it means longer and more fluent sentences, and maybe we should expect neural MT to lend us a hand with it. But if we are translating software strings or mobile content, life isn’t like that, is it? Our average word count per sentence is pretty low; our strings are mostly short (except for messages). So we may not get as much help from MT.
Maybe the average number of words per sentence would be an interesting number to take into account when we get a new project?
All of that said, these numbers would be auxiliary metrics. The word count that we use today may be looking at the end of its life. With adaptive technology and varying qualities of MT suggestions, it will become very difficult to associate all the possible variations of those suggestions to overall word count.
After all, fuzzy matches are based on translation memories, on a predetermined percentage of similarity to an entire segment previously translated. How would we use such a strict concept for all the different parameters when working with MT and adaptive technology?
Today, most of the time, the combination of MT and fuzzy matches constitute a system that outputs some mixture of “fuzzy grid for 75 / 85% and above” and “discounts for MT suggestions below that threshold.” End-clients and agencies calculate edit distance to get an informed view of whether the MT is still helping us (or if it is helping too much). And that is the system: “fuzzy with some MT discount.” Edit distance is also great to detect patterns that will improve MT output: if we’re making lots of changes, the MT may need some improvement.
But with adaptive technology and who knows what other forms of AI-powered augmented translation, how are we going to fit the fuzzy grid into this? We won’t. Translators will be paid based on time spent working. So everybody will change how they work and everybody will win. Dear translators, start thinking how much your hour is worth. It is about time.
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Renato Beninatto is the founder of Nimdzi Insights, one of the language industry’s leading analysts and consulting firms. He has over 30 years of executive-level experience in the localization industry. He has served on executive teams for some of the industry’s most prominent companies.
Renato is known for creating innovative strategies that drive growth on a global scale. He is an Ambassador for Translators Without Borders and hosts the award-winning Globally Speaking podcast. He is also the author of the book The General Theory of the Translation Company.
In this video provided by UTIC, Renato talks about his new book, “The General Theory of the Translation Company”.
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Scammers exist in a variety of industries, including the translation industry, unfortunately. They are the scourge of real translators and hiring managers alike, damaging reputations and wasting time and money. But now, one well-organized group is fighting back.
What’s the Scam
In the basic version of the scam, scammers steal CVs from real translators and then change the listed email address to a free email address the scammer controls. They may also partially change the CV, or cobble several stolen CVs together to “create” a new one. They then offer their “translation” services under the stolen or made-up identities.
Unfortunately, these scammers depend on uninformed clients, project managers and hiring managers not doing their due diligence and making terrible hiring decisions.
Once a scammer secures a job, the translation is done very shoddily or with Google Translate, for example, in the hopes that the manager, client or company doesn’t notice fast enough. But if someone complains, the scammers make threats or vanish, and then quickly move on to a new victim.
At a minimum, these scammers are guilty of identity theft and fraud.
Working in the communications industry for several decades, I’ve found real translators to be a pretty passionate bunch: about language, about communication and about finding just the right words to express a thought.
Unfortunately, translation scammers are making it more difficult for good, hardworking translators to fight through the noise and make a living. At the same time, scammers are making it more time-consuming for translation agencies to find the right, qualified translators.
In fact, we’ve received many fake CVs (and very terrible cover letters) from scammers over the years, but the professionalism and rigor of our hiring managers has ensured that we have never hired one of them.
Responsive Translation has a thorough vetting system for all new translators we hire — not to mention the quality assurance practices we have in place throughout the translation process.
Some of our translation projects are created by teams of translators, but absolutely every translation we do is checked and improved by several editors to ensure that no errors (and definitely no scammers) have gotten into the mix.
Exposing the Scammers
Slator recently wrote about the Translator Scammers Directory. This website, compiled by the volunteer Translator Scammers Intelligence Group, exposes the activities of translation scammers. There they make public (and Googleable) fake CVs, fake profiles and the email addresses some scammers use. They also provide tips on spotting fake CVs and how to fight back.
They hope that by shedding light on the scammers’ practices and promoting awareness, they will help to reduce the damage scammers are causing around the world.
View RESPONSIVE translation services blog >>
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I’m here with Dame Wendy Hall, Kluge Chair in Technology and Society, Regius Professor of Computer Science at the University of Southampton and early pioneer in web protocols; with Alexandre Loktionov, AHRC Fellow at the Kluge Center and an expert on hieroglyphic and cuneiform legal texts; and with Jessica Lingel, Kluge Fellow, assistant professor at the Annenberg School for Communication at the University of Pennsylvania and an expert on social media.
We ventured into talking about emoji and social media during a hallway conversation and thought it would be fun to pursue this further via blog.
The text of our Google Docs conversation was edited for length and clarity.
DT: There is much to explore, but it began with emoji, so let’s start there: elevated art form or corruption of language?
AL: For me, they’re essentially hieroglyphs and so a perfectly legitimate extension of language. They’re signs which, without having a phonetic value of their own, can ‘color’ the meaning of the preceding word or phrase. In Egyptology, these are called ‘determinatives’ — as they determine how written words should be understood. The concept has been around for 5,000 years, and it’s remarkably versatile because of its efficiency. You can cut down your character count if you supplement words with pictures — and that’s useful both to Twitter users today and to Ancient Egyptians laboriously carving signs into a rock stela.
DT: How does everyone feel about using emoji to write literature? The Library of Congress acquired an emoji version of none other than “Moby Dick” just a few years ago.
AL: I think you can definitely write literature with emoji — the question is, who will be able to read it? Do we have enough standardization in sign deployment? I think a full emoji dictionary/sign list would be necessary, unless, of course, we want to create a literature with multiple strands of interpretation (in a literal sense — where people see the same signs but interpret them in different ways).
JFL: I think part of it is about a fascination with how technology may be reshaping cultural production. I’m thinking of games around Twitter and literature, for example; the Guardian ran a challenge asking authors to write a story in 140 characters or less. (There’s a long and wonderful history of literature produced through challenges/games like these; I’m thinking of Shelley and Hemingway.) At the root, I think, is an anxiety around what it means to make art and how technology is making art better or worse.
DT: I’m optimistic because I see technological innovations opening up the range of what is possible artistically — Gutenberg, and so forth. On the other hand, certain technological turns have been very specific in their application. Think of Morse code: incredibly useful in certain contexts, but unlikely that we will ever write a novel in Morse.
AL: I think that gets to the heart of it — we have to think of the purpose of the means of communication, and in the case of emoji, we as a culture need to decide what they are: do we want them to be a bona fide script with full capability, or are they just a tool reserved for very specific purposes (alongside conventional means of writing)?
JFL: I don’t know about Morse code novels, but Morse code poetry is definitely a thing.
AL: It’s also worth thinking about canonicity — can emoji become canonical, in a way in which originally purely utilitarian hieroglyphs could after several millennia? Are we in this for the long run?
DT: Right, will there ever be an emoji dictionary? Perhaps there is already?
WH: There is a crowd-sourced emoji dictionary. It’s not very helpful at the moment, but then, neither was Wikipedia initially.
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The ProZ.com moderator class of 2016-2017 is coming to an end, but before this happens, ProZ.com would like to thank all of those members who have given of their time to help maintain a positive, results-oriented atmosphere on the site. Each person in the class has made valuable contributions to ProZ.com, and some of them even beyond the moderator program.
ProZ.com moderators are volunteer members who have benefited from ProZ.com and have chosen to give something back by playing their part, in turn, in a system put in place to ensure fair play. Their role is to foster and protect the positive, results-oriented atmosphere that makes ProZ.com possible, by:
- Greeting and guiding new participants, and helping them to properly use and benefit from what is available to them at ProZ.com.
- Enforcing site rules in a consistent and structured manner to maintain a constructive environment.
The moderator class of 2016-2017 is certainly a very good example of the role. Thank you mods!
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[UPDATE 1530 CET 2017/05/15: The report in question has now been published by Adzuna (link) ]
CRACOW, Poland, May 15 —England’s Daily Mail apparently has an exclusive on the end of the Translation & Localization Industry as we know it. If the British ‘tabloid’ is to be believed, the end is not merely nigh, it’s already here: according to an admittedly ungooglable “study from jobs search engine Adzuna” of “79 million job adverts placed in Britain in the previous two years,” robots are already taking human translators’ jobs on a “grand scale,” and with blame/credit belonging primarily to “Google … among those to have designed automated translation software, which is making human translators increasingly redundant.”
The news also made it around the Commonwealth, being picked up this morning by the Australian, who also failed to link to or otherwise properly reference the ephemeral report. Nevertheless, it ominously quotes UK job site Adzuna co-founder Doug Monro as predicting, “Automation is already replacing jobs and could be set to replace some roles, like translators and travel agents, entirely.”
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In the past few months free online translators have suddenly got much better. This may come as a surprise to those who have tried to make use of them in the past. But in November Google unveiled a new version of Translate. The old version, called “phrase-based” machine translation, worked on hunks of a sentence separately, with an output that was usually choppy and often inaccurate.
The new system still makes mistakes, but these are now relatively rare, where once they were ubiquitous. It uses an artificial neural network, linking digital “neurons” in several layers, each one feeding its output to the next layer, in an approach that is loosely modeled on the human brain. Neural-translation systems, like the phrase-based systems before them, are first “trained” by huge volumes of text translated by humans. But the neural version takes each word, and uses the surrounding context to turn it into a kind of abstract digital representation. It then tries to find the closest matching representation in the target language, based on what it has learned before. Neural translation handles long sentences much better than previous versions did.
The new Google Translate began by translating eight languages to and from English, most of them European. It is much easier for machines (and humans) to translate between closely related languages. But Google has also extended its neural engine to languages like Chinese (included in the first batch) and, more recently, to Arabic, Hebrew, Russian and Vietnamese, an exciting leap forward for these languages that are both important and difficult. On April 25th Google extended neural translation to nine Indian languages. Microsoft also has a neural system for several hard languages.
Google Translate does still occasionally garble sentences. The introduction to a Haaretz story in Hebrew had text that Google translated as: “According to the results of the truth in the first round of the presidential elections, Macaron and Le Pen went to the second round on May 7. In third place are Francois Peyon of the Right and Jean-Luc of Lanschon on the far left.” If you don’t know what this is about, it is nigh on useless. But if you know that it is about the French election, you can see that the engine has badly translated “samples of the official results” as “results of the truth”. It has also given odd transliterations for (Emmanuel) Macron and (François) Fillon (P and F can be the same letter in Hebrew). And it has done something particularly funny with Jean-Luc Mélenchon’s surname. “Me-” can mean “of” in Hebrew. The system is “dumb”, having no way of knowing that Mr Mélenchon is a French politician. It has merely been trained on lots of text previously translated from Hebrew to English.
Such fairly predictable errors should gradually be winnowed out as the programmers improve the system. But some “mistakes” from neural-translation systems can seem mysterious. Users have found that typing in random characters in languages such as Thai, for example, results in Google producing oddly surreal “translations” like: “There are six sparks in the sky, each with six spheres. The sphere of the sphere is the sphere of the sphere.”
Although this might put a few postmodern poets out of work, neural-translation systems aren’t ready to replace humans any time soon. Literature requires far too supple an understanding of the author’s intentions and culture for machines to do the job. And for critical work—technical, financial or legal, say—small mistakes (of which even the best systems still produce plenty) are unacceptable; a human will at the very least have to be at the wheel to vet and edit the output of automatic systems.
Online translating is of great benefit to the globally curious. Many people long to see what other cultures are reading and talking about, but have no time to learn the languages. Though still finding its feet, the new generation of translation software dangles the promise of being able to do just that.
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What do a vice-presidential debate, the discovery of Richard III’s bones or the 9/11 attacks have in common? According to Peter Sokolowski, editor for Merriam-Webster, these can be considered ‘vocabulary events’ that make readers run to their dictionaries.
In 1996 the company that had published the largest and most popular college dictionary decided to make available some of their content online. Since then, Merriam-Webster Inc. has been monitoring what words readers search for and discovered that there was an increase in the searches for specific words during major news events.
This started after the death of Princess Diana. According to Sokolowski, “the royal tragedy triggered searches on the Merriam-Webster website for ‘paparazzi’ and ‘cortege’”. Another example is the word ‘admonish’, which became the most looked-up word after the White House said it would ‘admonish’ Representative Joe Wilson for interrupting a speech by President Obama.
Certainly none of this tracking would be possible without the transition from print to digital era. Some of the leading publishers such as Macmillan Education have already announced that they will no longer make printed dictionaries and others are looking for partnerships with Amazon or Apple. This means that, whether you are using your computer, e-book, tablet or smartphone, any dictionary is just a click away.
And what is the purpose of monitoring dictionary searches?
Every time you look up a word in the Merriam-Webster website you give valuable information to lexicographers about terms that could be added or that need to be updated in their dictionary. The most looked-up word also provides data about the public’s strongest interest. This approach can also be found in other online dictionaries that are open to receive suggestions on new words or new usages of old words, the same way as James Murray and his team did with the first Oxford English Dictionary in the 19th century.
In other words it is ‘crowdsourcing’ applied to lexicography.
Even though there are many advantages in using online dictionaries, some will still miss the feeling of searching through the pages of a printed version or finding a random word. However, the digital era gives us the possibility to update information progressively as needed. A similar attitude is found in proactive terminology, which encourages terminologists to identify the topics that are likely to come up so they can provide translators with the terminology that will be needed.
So, the answer is yes! Somehow our dictionaries are reading us.
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Language is the original network technology. When someone learns a language I speak, I benefit because of expanded possibilities for interaction. The long-distance communications revolutions since the 19th century increase the strength of the network effect. These days an English speaker can travel the globe, either in person or from the comfort of a web browser, and interact with others who speak English, either as their mother tongue (372 million) or as a second language (612 million) (Ethnologue).
The globalised Anglosphere we are all familiar with stands in stark contrast to the tremendous diversity of mother tongues spoken around the world. There are more than 6,500 distinct languages in use today. We measure the size of a language by counting the number of people who speak it as a mother tongue. There is enormous variation in the size of languages. While the sixteen largest account for half of the human population, there are more than three thousand small languages spoken by fewer than 10,000 people.
The network effect, reinforced by modern communications technologies, would seem to favour the consolidation of human beings on to a much smaller set of spoken languages, posing a threat to the continued survival of the vast majority of the 6,500 languages in use. But is that actually what is happening? In work recently published in The Economic Journal, I bring two data sources to bear on the question of whether the world’s languages are consolidating. These sources allow me to address the question from different angles, and both provide the same answer. Language consolidation does appear to be underway, but only for those languages with fewer than 35,000 speakers. That means that around 1,900 languages are large enough to be under no threat at all. I conduct simulations using the relationship between language size and growth that suggest about 1,600 languages will become extinct in the next 100 years.
There are two ways to look at these results. On the one hand, the extinction of a quarter of the world’s extent languages would represent a significant loss of human cultural diversity. From that perspective, language consolidation appears as a significant problem. On the other hand, it is striking just how small the minimum viable size for a language remains in a world with such cheap and easy long-distance communication. A settlement of 35,000 people would be considered small almost anywhere in the modern world. That such a small group could maintain its own language in a globalised world is remarkable.
Given the power of these technologies, why are people not abandoning languages that connect them with only 50,000 or 100,000 other people? The answer to this question is less certain, though there are three likely explanations. The first is that much linguistic communication is face-to-face and thus very localised. Above all else, one must be able to speak with others in one’s family, those one works with, and members of their local community. For the vast majority of human beings, those interactions happen within just a few miles of where they live. Second, many goods that can be produced far away, such as clothing and food, do not require knowledge of another language to consume. Third, bilingualism in a second, more widely spoken language need not lead to displacement of a small-sized mother tongue over time. Indeed, a small cadre of bilinguals can serve many of the external communication needs of a small language community.
The data I use primarily reflects conditions at the end of the 20th century. It therefore does not reflect changes that may have come or will come with the wider diffusion of the internet. Only 178 languages, a mere three per cent of the total, have any content at all on the internet. Only 11 per cent of the world’s internet users come from English-majority countries, more than half of all web pages are in English (W3Techs and WDI). While it is possible that the internet may increase the minimum viable size for a language, my suspicion is that the main result will be to promote more bilingualism. Consider the case of the Netherlands, where knowledge of Dutch is under no threat despite more than 90 per cent of the population being able to speak English.
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If you use WordPress for your web site, you may be interested in this explanation of multilingual plugins and ranking of the 19 best multilingual plugins for 2017:
19 Best WordPress Multilingual Translation Plugins for 2017
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“Mansplain” made its way into the Urban Dictionary in 2009. In 2010, “mansplainer” was a New York Times Word of the Year. In 2014, Salon declared the word dead (the true sign of making it); the Oxford Dictionaries added “mansplain” as an entry; and the Macquarie Dictionary named it Word of the Year.
At its most basic, “mansplaining” refers to — as a 2015 Merriam-Webster “Words We’re Watching” column put it — “what occurs when a man talks condescendingly to someone (especially a woman) about something he has incomplete knowledge of, with the mistaken assumption that he knows more about it than the person he’s talking to does.”
Although the term “mansplaining” originated in the United States, the practice may very well be universal — and in fact, the term has already moved abroad. In 2015, the Swedish Language Council welcomed “mansplaining” to its list of new Swedish words. Iceland made its own variant (“hrútskýring,” or “ramsplaining”) the 2016 Word of the Year — and named a beer after it. In Greek, Japanese, Portuguese, Swedish, and many other languages, the English “mansplaining” just gets dropped into the conversation, and folks nod.
This list was crowdsourced among friends, writers, and scholars, who reached out to their own friends and families around the world to collect the words on everybody’s lips — and even to coin a few. Like the original term, new words for “mansplaining” get invented on the fly, sometimes in a single, offhand tweet. From that point of origin, they go viral on social media, or get adopted by a national tourist board, and finally make their way into lexicons.
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An interesting reflection from Tim Parks on the expendability of translators (from a commercial point of view) and the contentious issue of what a book’s translator deserves to be paid. Though everyone might agree that translators should be better paid, do we think of translation as its own intellectual property, and therefore translators deserving of a book’s royalties, like an author is? Or would it be better to be paid based on the difficulty of a translation, which likely has nothing to do with how commercially successful a book is (but everything to do with how long the translation takes)? Or will certain literary translation always be a labour of love? ( I was interested find out that 0.10 euros a word is a going rate for top-quality literary translation …)
From the article:
“Krieger eventually won her case and the money she was owed, but the sequence of events suggests the essential difference between translators and authors: [the publisher] Piper could never have tried to deprive [the author] Baricco of his royalties, since without him there would have been no books and no sales. He was not replaceable. But however fine Krieger’s translations, the publisher felt that the same commercial result could be achieved with another translator. It’s not that translation work is ever easy; on the contrary. Simply that it rarely requires a unique talent. Krieger wasn’t essential. She could be replaced.”
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