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PowerPoint torture
“Japanese clients use PowerPoint for the wrong reasons; they stick as much information as possible onto a single slide, and it’s hell on the translator who’s told to use the same layout for the English.”
Sound familiar? Well here’s a little something Younghusband at Coming Anarchy has shared with us:
>Doesn’t look like the Japanese have a monopoly on insane levels of data presentation per PPT slide. And yes, of course it’s the US military that has gotten in on the game. (The image above is linked to a PDF of the full presentation, if you want to view it and ponder what an E-J translator might feel at the sight of this monstrosity.)
Real soon now
Web comic XKCD weighs in with an observation that applies nicely to the quest for convincing machine translation:
Et tu, Barack?
Yet another article on how computer technology will save us all from the tyranny of having humans in charge of the task of human communication. A BusinessWeek piece titled “White House Challenges Translation Industry to Innovate” tells the tale:
Companies have combined the power of humans and computers to simultaneously double the speed of translation and nearly halve its cost. Where each translator once converted 2,500 words a day at a cost of some 25¢ per word, they can now offer 5,000 words a day at around 12¢-15¢ a word.
Marvelous. This translator makes the same amount of money per day, according to this math, but turns out twice as much text in the target language. Efficiency up, global understanding up. But there are problems here. A few quick, unorganized thoughts:
Problem 1: We aren’t worrying about the fact that this means only half as much time can be spent on proper rereading by the translator and editing by a fresh pair of eyes. The hybrid approach of MT to begin and a human to polish the turds that are MT output means there’s an unhappy person in the mix now—at least I don’t think many people are happy about wrestling with clumsily translated text. I can’t stand it when I’m dealing with stuff a human put together, and even that clumsy human translator is leagues ahead of a machine, and will remain there for the foreseeable future.
Problem 2: The editors who deal with machine output are, ideally, bilingual and capable of doing the translation themselves. If something looks truly odd in your target text, going back to the source text to figure out what’s going on is the only way to set things straight. (Well, there’s actually another way: the monolingual editor just makes a wild guess. I didn’t say it was a good way.) In other words, the ideal form of this man-machine mind meld involves taking a translator who used to be crafting his own sentences and making him clean up the ones a computer spits out at him. Job satisfaction in this new world? Heh.
Problem 3: Don DePalma, chief research officer at a translation outfit, notes that companies need to get their information out there in front of customers in their own languages. “When you’re dealing with anything really expensive or that potentially involves a long-term financial decision—like life insurance or stocks—customers prefer to have information in their own language,” he says. But this is precisely the sort of text that needs to be handled by a specialist, and the companies that sell “really expensive” products will be the very last holdouts still using human pros for the entire process. (It would be fun to see someone trying to market life insurance via Google Translate and an editor in Bangalore, though.) It’s fine to trot this out as proof that companies will need to pay more attention to localizing their material for various markets, but it’s a poor example to bring into the “MT is the future” article.
Problem 4: This.
With [human-assisted machine translation] systems, text is fed into a computer program that tackles the first round of word and sentence conversion using statistics, language rules, or matching with past translations. That covers about 90% of the work. A human then steps in to correct mistakes, clarify sentences, and refine the language for the intended audience or market.
Anyone who’s done translation (at least at a level going beyond churning out crap drafts for rock-bottom prices) or editing knows that the 90% figure here is sheer idiocy. Experienced translators don’t tend to work in phases like this (pump out rubbish at blinding speed and then go back to correct spelling and grammar errors and think about tone and style); they have all these tasks in mind as they go through their text, and it’s hard as a result to define percentages for the effort going into each one of them. But I think the thing that makes translating between human languages a steep challenge for computers is the need to “refine the language for the intended audience or market.” Computers can’t recognize context like that. Humans can, and for human translators, keeping that context in mind and crafting a target text that meets the needs of style, readership, and client preference accounts for vastly more than 10% of their effort. I’d suggest flipping this formula around and saying that the computers handle a tenth of the work, not nine times that amount.
Problem 5: “Language translation is far from being mastered by humans, computers, or any mix of the two.” This is just annoying. It reeks of creationists’ “teach the controversy” demands for equal time for unequal worldviews. Using languages to communicate is what humans do. Birds fly. Fish swim. We talk. What mastery there is in the field of translation belongs entirely to people, and articles like this one need to be written from the perspective of how close computers are to reaching that standard.
Anyway. Enough problems. I’m of two minds when it comes to predicting the future of machine translation. On the one hand, I think the human capacity for language is too deep and too broad for machines to ever take it over completely, and even if 90% of clients end up happy with dirt-cheap mediocrity, the 10% of clients still paying for human quality will represent a healthy chunk of a growing language-services pie. So the good translators will still be making money, and it won’t be by massaging the output of a Google data center.
On the other hand, though, if the scientists ever crack this mystery wide open (perhaps by giving up on computers with nothing but 0s and 1s to deal with and creating new machines that function more like a brain) then we’ll get our translating machine. I’ll be out of a job, along with all my translator and interpreter buddies. But of course we’ll have plenty of company in the unemployment lines, since computers with real thinking power will already have taken over more menial tasks like piloting airplanes, writing software, drafting legislation, teaching children . . .
Mangled Mifune on the Menu
A very quick heads-up to a translator who wants to make an interesting (and potentially delicious) cold call: 料理屋「三船」 (Ryōri-ya Mifune) is a restaurant that takes the great actor Mifune Toshirō as its theme. Part of me hopes this means Belushi-style swordplay à la “Samurai Delicatessen,” but the place looks like it’s shooting for something a bit more classy.
Expressing a view of Mifune Toshiro’s world in to the plate, and the food he loved the most, to your satisfactory.
Unfortunately, the class doesn’t extend to the English portion of its website. Hence the translation sales angle. Go for a meal, thank the owner for the culinary experience, and mention that you’d like to help brush up the multilingual site so it matches the stature of the man whose name is being borrowed.
(Via @Nictos.)
LinkedIn’s localization problem
LinkedIn is supposed to be like Facebook for grownups. A place to network online in a business context. “Our mission,” the site purposefully states, “is to connect the world’s professionals to accelerate their success.”
This makes its latest overture to the translators among its membership an offensive one. This post gives the gory details, but to summarize, LinkedIn invited translators to participate in a user survey, which kicked off with questions gauging their interest in helping localize the site for other language markets and punched them all in the face with the “how would you like to be compensated?” reply options: a badge to put on your profile, an upgraded account, or just the satisfaction of having helped out. Nothing involving money.
A number of translators immediately filled the “additional comments here” bit at the end of the survey with snide, insulted, and angry comments, to judge from the reactions I saw on Twitter. The fallout for LinkedIn has been a considerable drop in its image in the eyes of translators and booming membership in the “Translators against Crowdsourcing by Commercial Businesses” group hosted right there on the site. (To his credit, LinkedIn representative Nico Posner has been posting in that group’s discussions and trying to explain the company’s move, but the explanations aren’t swaying anyone.)
Why is this? It isn’t as though LinkedIn is the first outfit to try to get a bunch of its content translated for free in this way. The TED Open Translation Project gets the TED conference talks translated and subtitled in a range of languages, all by volunteers. (The site also notes that this project is “generously supported by our sponsor Nokia,” but I guess that generosity doesn’t extend to money for the people doing the work.) Since the TED videos are all released under a Creative Commons license, though, they are free to use, repost, and share with others, so it isn’t as though the TED people are trying to make money off of the labor of the folks producing the translations.
I wrote last May about the way Facebook got its site interface translated into various tongues through the crowdsourcing approach. Here we’re getting closer to the unpleasantness that is a for-profit firm begging for freebies. Two differences, though: First, the Facebook approach used pro translators toward the end of the process, to go over the close-to-final output and make sure it wasn’t still amateurish. (This is admittedly something LinkedIn could be considering.) Second, it fired up a translation application and let any and all comers jump right in and take part, rather than asking experienced translators to do the grunt work without pay. (Which, it should be noted, has not been without problems.)
Which leads me to the biggest mistake that LinkedIn made. The company actually sought out professional translators from among its membership and approached them with this survey. Rather than open the door to college students, who might be interested in the task and have the free time to take it on, or be interested in the potential minor benefit of a “I helped out” badge on their LinkedIn profiles when they try looking for a job through the site one day, the company came to people who can and do charge real money for exactly this sort of job and gave them the survey with no mention of that cold, hard cash. It’s not hard to see why nobody in this group is viewing this as a way to “accelerate their success.”
Anyway. I imagine that LinkedIn will eventually rope some translators-in-training into this job, save money and please the investors, and get localized for a number of new markets. But at the cost of some good will among the language professionals who until this week thought more highly of the site as a place to develop their professional identity online.
Semi-related aside
Chris Salzberg (until March this year one of the authors at Global Voices) gave a presentation in Tokyo recently on social media and translation, in which he pointed out that a group of 240 unpaid Chinese translators translate every article in every issue of the weekly Economist. (Not having attended the talk, I can’t say for sure whether he successfully made the connection between “groups successfully do translation projects on a volunteer basis” and “there is a business model” that his slide seems to suggest.) Chris has an article online at Translation Journal that’s also worth reading if you’re interested in cooperative translation efforts. When you’re talking about purely volunteer work for worthy causes, or unsolicited translations of a magazine that isn’t going to be localized and marketed in a certain country anyway, there are certainly situations where crowdsourced translation plays a valuable role.
Super bonus humor postscript
The About Us page on LinkedIn comes complete with a video whose subtitles you can change to a number of languages, courtesy of whoever produces the things with the tools over at dotSUB. The Japanese subtitles are quite obviously the result of machine translation with minimal human editing, or perhaps a second-year Japanese student with access to Jim Breen’s glossaries. If you read Japanese, by all means check them out; if you can’t, go to a site like Engrish.com and envision the same sort of thing, going the other direction. (I stuck screencaps of the subtitles over here in case you just want the text without the sound and newfangled moving pictures. Clicking the “Video Transcription” bar at the dotSUB page will get you the text data; make sure you select the language you want in the video frame first.)
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