Category Archives: Artificial Intelligence

Talking to machines

Ever took a robocall? Pretty annoying, huh? A prerecorded message sounds on the other end of the line after a machine calls your phone number at dinnertime. The next morning, you need to call the DMV because you changed addresses. It is seldom a live human voice who answers the phone. We are all painfully familiar with the stock phrase offering us a language option: “Press 1 for English, press 2 for Spanish…»

AT&T and other companies save a lot of money by using IVRs (interactive voice response systems). A computer, not a human operator, interacts with a caller and responds/routes calls according to the nature of the query. Call centers fully operated by humans are costly to run. One way to reduce this cost is to outsource the customer service (or technical support) to a cheaper call center overseas. You or someone you know have already experienced this in the form of a support call for a company like Dell Computers taken overseas by a India-based call center. The guturally-accented English is noticeable. I have personally met some people at call centers in Córdoba, Argentina. Among their customers are cellphone companies. These employees have studied British English in college, which shows as a slight accent. This can be very annoying to a customer who is already irate about poor service.

In the genial movie Wargames, the term machine is mentioned by various characters in different situations, but the viewer gets the impression that there’s a question mark attached to the seemingly evident advantages given by our wondrous technologies. In the movie, a fully-automated computer system called WOPR (War Operation Plan Response) is in charge of controlling the launching of nuclear missiles, eliminating the need of human intervention. At one point, the WOPR detects (erroneously) that a Russian nuclear missile attack is under way. General Beringer, in charge of NORAD, asks Dr. McKittrick what the WOPR recommends, and the response is “Full-scale retaliatory strike.” Bemused and sarcastic, Gen. Beringer responds “I need some machine to tell me that?”

The WOPR system at NORAD in the movie Wargames

This could point to one of the morals of the story: Do we need a machine to tell us the obvious? Take some feature of your word processor, for example: if the spellchecker says it’s okay, it must be okay, right? And some automated features can become a hindrance to productivity and performance. I had a taste of this last week when I was readying a file to be exported back to Word format for a rush job. Because of some incomplete or corrupt codes, which I couldn’t immediately fix, the program repeatedly and consistently failed to export the file. It took me a few minutes of fiddling with the options until I fixed the problem, running against the clock.

Had I translated the document in plain wordprocessing fashion, with no CAT tool at hand, I would have not faced a corrupt code problem to begin with. But we translators also love technology, and the occasional hiccup is the price we pay for a more streamlined (irony intended) performance.

A few months ago, I ran into a more intractable problem. I was setting a Burmese translation in an InDesign CS3 document. Not knowing Burmese —a beautiful script, with elegant strokes and fanciful characters—, I first struggled with the correct font to display the characters correctly and then with the ligatures so that the words connected properly. Had I worked with a handwritten copy, I would have just erased the offending stroke, line or letter and rewrite. But a complex software like InDesign automates things like ligatures, kerning and other font features. It took me hours to get things right. Despite my technical knowledge, I still had to send a PDF copy in Burmese for approval by a human Burmese translator to make sure the script looked right, prior to final delivery.

You trust your dryer to do a proper job with your clothes but, would you trust a robot to paint your house? Surely you do online banking and do your taxes with the help of software, but, would you depend on artificial intelligence or ask a machine for financial advice? If you are single and looking, would you ask your friends to match you up with someone or would you trust online software in a dating site to match you up with someone? Would you carry on a love conversation with an Internet bot? Would you trust your company’s marketing tagline to a piece of software? Will you let software write up a sports column?

Actually, the latter scenario is already possible, thanks to Narrative Science‘s software. Last month, I spoke with Larry Adams, one of Narrative Science’s representatives, about the main features of their program, which mines data to author a piece of writing that is basically undistinguishable from what a human writer would create.

What if you need an email written in Mongolian translated into English in a rush? Enter Google Translate or any other number of software solutions, powered by machine translation. What drives the translation of large volumes of content, or bulk translation, is speed, not quality. Large companies that can afford the expense of custom-built machine translation software solutions already create multilingual versions of their technical documentation. Companies with a smaller wallet have to content themselves with us, human translators. For the sake of argument, I’ll oversimplify the issue a little bit. There are large translation companies that operate in bulk and outsource language services to the cheapest providers, from India to Argentina. Other companies try to stay competitive by emphasizing quality, then hire a more costly professional workforce in developed countries. The downward push on translation costs continue. After all, translation is usually viewed as a necessary cost of doing business, like buying office supplies or ordering printer ink cartridges.

While American business owners recognize the need and advantage of addressing the translation of documentation for their products or services, it is difficult for them to see the direct connection between higher sales and better-written translations. Hence, the advantages of quality translation are intangible ones, noble concepts in an abstract world. Companies with overseas offices trust their salespeople in the different geographies to check the accuracy of the translated documents. In-country reviews are an established quality control but translation managers often face an uphill battle to perform these reviews according to quality translation standards because the reviews’ completion depends on the time and availability of the reviewers —the people who are in charge of selling and marketing the products. Their main job is to market and sell, not to sit down and review translations, a task that is not a natural part of their role.

In the meantime, companies are offered a variety of technologies to automate most of the translation process: translation memories, terminology databases, automated quality controls, confirmed translations with lock-out of changes (so that future translators or editors cannot modify them once approved), and, of course, machine translation. As machine translation reaches new, more solid performance markers, a question insinuates itself: Will it be possible to completely automate the creative process of translation by sheer data mining and parsing of linguistic patterns in the corpus?

There is an intriguing article on self-driving cars in the latest issue of WIRED magazine. In the near future, it would be possible to let the driving to an advanced vehicle. Software solutions devised by companies like Narrative Science may make the high cost of writing standard sports news and financial articles a thing of the past, once the engine is properly customized. There seems to be a technological answer to our most pressing problems. Will translators be relegated to the mere role of editors, no more creators of original translations?

Machines and software, regardless of their level of automation, still operate in a GIGO fashion (garbage in, garbage out). The machine is no better than the operator that programmed it. Intuition, creativity, the right turn of phrase, the cumulative good judgment that comes from years of writing experience cannot be automated. Your business uses complex software and complex machines to churn out products and project sales. But, who do you turn to for sales, marketing or financial advice vital to your business? A machine? A software bot?

Towards the end of the movie Wargames, General Beringer faces a crisis. The highly sophisticated WOPR system warns of an impending Russian attack in the form of 2100 missiles, which may or may not be a simulation. The general is torn between ordering an attack for real and assuming that it’s a computer game gone awry, while the U.S. president is waiting for a decision on the phone. The creator of WOPR, Stephen Falken, reasons with him in this moment of terror:

-General, you are listening to a machine. Do the world a favor and don’t act like one.

Wise words to live by.

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Filed under Artificial Intelligence, Deja Vu X, Machine translation, QA standards, Trados

In search of the holy grail of non-human translation

In Slate, Jeremy Kingsley writes about Google Translate. The tagline reads It already speaks 57 languages as well as a 10-year-old. How good can it get? (Read the article here).

My answer: not that good. How can a 10-year-old be a good writer, unless he’s a prodigy? More to the point: you may be fluent in German, but that doesn’t mean you can write in German appropriately in a given situation, like an educated native would. Most proponents of machine translation (MT for short) are enamored with having software produce translations after learning foreign languages. Here’s the problem: translation has little to do with learning a foreign language, and a lot to do with the craft of writing, acquired after years and years of practice and error.

I was intrigued, however, by Mr. Kingsley’s article, to which I responded in the following fashion:

Mr. Kingsley is evidently enthusiastic about technology marvels that may or may not replace some activities of the human brain. I don’t blame him, he’s just a writer.

Even though the article brings together different views (Bello and Wittgenstein), it struggles to be neutral…and fails. There are so many aspects that pop up in a well-informed conversation about machine translation that my comments cannot possibly touch on all of them, but here’s my attempt:

a) Orality (the speech part of language) informs but does not shape all forms of written expression in a language.
b) Most languages have a written form, some never had one. Where would Google Translate (or MT) find the copious amounts of data to mine? Nowhere.
c) Human knowledge and activity show themselves in thousands of domains, not just EU documents, not just webpages. How many books are NOT in digital form? The Internet’s corpus is minuscule by comparison.
d) Different domains (law, financial prospects, discovery documents, material safety data sheets, voting instructions, and so on) have different registers, different formulas for expression. Some languages handle similar situations in different ways, with a different tone in writing form.

Translation is an act of written and visual creation. Before we get all enthused about how technology tools can “translate”, we should ask ourselves “can software write something cogently?” Or, “can software create?” If by creating we mean “doing something from scratch”, we already have robots that can perform such tasks. Obviously, there’s more to it than meets the eye.

To me, a created thing has to bear a meaning given by its creator. No, I am not talking about god or religion here. There’s meaning, intent, focus, tone, a sense of beauty or a tinge of ugliness, contradiction, coolness or fervor, a human imprint.

Of course, there are translation users who can’t be bothered with these disquisitions. As Mr. Kingsley said, their bar is low enough that they can achieve software-enabled translations to meet a need. Here’s a question: Who will bother to ask for input from the reader? Isn’t that the purpose of having a text translated?

There’s more. When you write, you decide what words to use based on a number of circumstances. Some words come to mind more easily than others, some phrases and references pop up more freshly or apt than others. In short, what you write is the sum of your decisions. What you translate is no different.

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Filed under Artificial Intelligence, Cultural awareness, Machine translation, Software-enabled translation, The craft of translation, Writing skills

Translators will always be wanted

I recently answered a poll at the popular site Proz. The question was Would you recommend translation as a career to future generations? There were over a dozen comments by the participants.

Understandably, some translators are concerned about finding direct clients or retaining the ones they got. Others doubt because technologies may replace our craft. Here’s my answer:

Absolutely, a resounding YES

I’ve been a full-time translator, often freelancing, sometimes inhousing, for the last 19+ years in America (oh, sorry, the U.S.A.) –I was born in Argentina.

I am not afraid of new technologies, Google, artificial intelligence or other tools because I don’t confuse excellent writing with so-called productivity. Translators who write very well are hardly in danger of being replaced by technology (how unimaginative!) or low-cost translators in third- and fourth-world countries.

Translation requires passion as do other professions and crafts, but excelling at writing in your own mother tongue is so germane to our occupation that you can’t be a good or successful translator unless you write very, but very well.

Our profession also requires an understanding and command of translation techniques and strategies, something you learn from translation theory. Don’t get me wrong, I am not talking about ivory-tower, only-for-academics theory. But it is required to understand why some texts can be translated in one way and other texts in another.

Finally, excellent translators know how to read and why (this reminds me of a Harold Bloom book I just purchased and that I am impatient to start reading!). My best friends are books (sorry, human best friends!). They’re always there, they help me reflect on what is said and how it is said.

Loving languages or being a polyglot are not enough to become a prosperous translator (I am using ‘prosperous’ here with liberty). You have to love to write, and write well. Anything else is secondary.

The poll and comments can be found here.

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Filed under Artificial Intelligence, Customer relationship, Customers, Marketing, Professional development, The craft of translation, Translation as writing

We’re in the fourth inning

Let me start today’s post by honoring a revolutionary world inventor, Steve Jobs, on his passing, just a day after the new iPhone 4S was announced by Tim Cook this week. Some bloggers and news outlets were underwhelmed by this new iteration of the famous gizmo. There is one experimental feature, however, that deserves special mention.

Siri is a new feature in the iPhone 4S. According to media reports, it allows the user to speak to the phone not just statements like “Call Stan to go watch Thor” but queries such as “Any Jiffy Lubes in Orlando?” as well. While Siri is in beta mode (in English only, I suppose), I can imagine the use of its artificial intelligence (A.I.) engine to infer meaning from statements in other languages.

According to Fox News Latino, Spanish will be a challenge for voice recognition in Siri. So, we are forced to sit on our hands and wait. If A.I. through Siri could interpret Spanish phrases and commands in a fairly accurate fashion, it won’t be machine translation per se, but a new kind of computerized, on-the-fly foreign language interpretation.

In yesterday’s issue of USA Today’s Money section, Mike Thompson, mobile business head at Nuance Communications, says “We’re in the fourth inning –the rate of change and innovation is faster than ever before in speech. The accuracy and performance [are] getting better. (In the) next five innings, we’ll see greater and greater natural language.” I was happy to find a technical text with a sports metaphor. This can be an excellent exercise in writing to test your Spanish writers and translators.

Spanish translators knowledgeable in baseball understand the meaning of inning, one of nine divisions of play. It’s called entrada or manga in Spanish. Most translation services providers like to talk about high quality, faithful translations. Almost no one says a word about translation strategies or techniques, which are learned through a rigorous study of translation theories applied to practice.

If we use the equivalence strategy, we could search for a sports equivalence in Spanish –soccer or baseball, perhaps? But what about the meaning underneath the sports figure of speech? What does the author say with in the fourth inning? What does he say with in the next five innings? It doesn’t take us long to realize that the fourth inning is very close to half of the baseball game at 9 innings, as if he were saying we’re almost halfway.

How would your Spanish writer or translator express this?

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Filed under Artificial Intelligence, Spanish language, Style