Jun 15, 2016

Better ways to search the Internet while translating

When Kilgray introduced memoQ Web Search a few years ago, I was unimpressed, because I was fairly efficient at working with the several tabs of my favorite sites to search for information during translation projects and I couldn't imagine much value to be had from an "integrated" search in a stripped-down custom browser. And the buggy example templates shipped with the memoQ release (several search setups are incorrect) didn't help much. It was only when I began to take a careful, systematic look at this feature to document it for my memoQuickies guide to configuration that I realized how straightforward it really was, and since then, despite ongoing bugs in the feature which sometimes lead to crashes, it has become one of the most important practical features of the product for me. Being able to select a text in the source or target and hit a hot key to search multiple sites at once really does save me time. Lots of it.

There has, of course, been another product around which does that too, which works in more or less any Windows environment and which is far more configurable. But when I took my first look at IntelliWebSearch (IWS) years ago, I was put off and confused by the nerdishness of the presentation, and I just wasn't ready to be told how many damned options I had when I was trying to get my head around the simplest basics. Recently, however, I have beome increasingly irritated at little things with memoQ Web Search, including the impossibility of adding my user credentials to turn off ads on some sites I use, and I began to wonder if IWS might not be worth another look. And indeed it is.

 Go to the IWS web site and learn more!

I am a big fan of multiple concordance search sets in integrated translation environments- this isn't a feature in any of them as far as I know, but it is accomplished easily enough. In memoQ I use the integrated concordance feature to search all the translation memories and corpora attached to a project for my "primary" concordance search set. The memoQ TM Search Tool is configured to search another set of TMs, including some with languages other than those in my project. And for blockbuster TM concordance searches with massive resources like the DGT data sets I have TMLookup. That is three differently configured concordance searches available at a keystroke in my working environment, and you can do the same thing in your favorite CAT tool as well.

So why not try this with web searches? Searching too many tabs at once tends to be slow, which is why I generally recommend no more than a few favorites be used with the integrated memoQ feature. IWS, unlike my usual tool, offers the possibility of configuring multiple "search groups", all of which can be accessed from anywhere with a hot key combination you assign. So I tried this for a few special sites that I usually don't want in my memoQ Web Search but which are a nuisance to deal with manually when I need them. It took me just a few minutes to install IWS, and with the help of a couple of short tutorial videos on the tool's web site, I had my custom searches set up(the configuration wizard to do this is dead easy and user friendly), and in about 10 minutes I was happily invoking special web searches on multiple tabs of my default browser (where I have configured some sites to shut off the damned ads) while I worked in the memoQ translation and editing grid.

So IntelliWebSearch today isn't nearly as difficult to figure out as it seemed years ago. Whether the product has improved or my head is just a little less cluttered now I'm not sure. But it's a very useful tool and a good extension of my working environment which I can recommend with more confidence. I can do useful things without drowning in the depth of its product features.

Nonetheless, I wouldn't mind a guided tour from someone with more of a clue than I have. And later this summer I have exactly such an opportunity. Colleague Michael Farrell, the Italian to English translator who created IntelliWebSearch for all of us, is giving two webinars sponsored by IAPTI, which offer a thorough grounding in the basics of this productivity booster. Information with links (click the images!) is below. I'll be there and hope you'll join me and learn useful things to help in your research of monolingual and multiplingual information sources on the Internet.

 Registration for the webinar

 Registration for the second webinar

Jun 10, 2016

memoQ 2015 - big improvements, ongoing issues


The pace of life is slow in the Algarve, but memoQ users would generally agree that big TMX imports to memoQ are a lot slower. Until now.

While on holiday I got a note from an occasional client, asking if I could take on the translation of a little Interessenabgleich for the next day. Not a problem I thought, though I had not yet imported the backup of my Big Mama TM with legal content onto the pokey Portuguese laptop I had dragged with me. With only 4 GB of RAM and the megatrojan known as Windows 10 it isn't much for crunching big data, but it is satisfyingly slow for demos and teaching classes, giving me plenty of time for questions and explanations while things load or refresh.

Nearly three hours later, the import was finished. So was I by that time; it was much too late to think of work any more. And the next morning, as I furiously worked to meet the deadline, a new update for memoQ appeared, which I was too muddle-headed to put off until I had delivered. After updating, not only was my license no longer recognized, I could not even boot the program to re-enter it! Welcome to Build 152! On another machine updated remotely, the update worked, but there was a funny conflict with Kilgray's standard template designations, causing the templates to be re-named.

The problems were sorted out by re-installing the build with a downloaded installation file from memoQ.com, and the job made it out on time. More glitches with term base editing and addition showed up later that day as I worked on another text. Grr.

Fortunately, Kilgray responds quickly to most issues, and the automated bug reporting features recently introduced are probably a great help in pinpointing troubles which users are too often reluctant to complain about. The next morning a patched version - Build 153 - was released. So far, so good with it.

The latest build introduces some interesting new features: among them are content extraction of more Microsoft Office object types, including equations (I've waited years for this!!!), for translation, faster TM imports, word count weighting, better tag handling, improved QA checks and some other things. Looking at all this will take some time, but it will be time well spent for my work purposes.

I was intrigued by the statement that "importing a TM with really many segments has just gotten up to 70 times faster", so I decided to test that with the file that bedeviled my evening recently. The problem with marketing hype is that it emphasizes exceptional cases and too seldom reflects what a typical work experience might be. And here too this is the case. The import of approximately 330,000 segments was completed in just under half an hour, about six times faster. Perhaps on my souped-up, RAM-rich machine in the office the difference might be more dramatic, but that is still a great improvement which will make me less hesitant to do maintenance on my large data sets.

memoQ 2015 has had a rockier road than any other version since the infamous memoQ Version 6 for which the code base was rewritten; a bit over a year since its release it still surprises me with inconvenient errors at inconvenient times, in contrast to my usual expectation of stability and reliability about four months after a major release. Actually, memoQ 2015 seemed to get to that point early in my working experience until about five months after its release, when in late autumn last year things went to Hell with SDL compatibility in many projects. Interesting new features, such as regex filters for the working grid and find/replace functions, continued to be added, but it all had a bit of a bleeding edge feel.

Now that memoQ has survived the awful transition to the me-too ribbon interface, it really is time to focus on stability and process reliability. The tool has become a major part of major corporate translation operations as well as the daily work of countless independent translators and we need stability to support our business more than we need new technotunes to whistle. I appreciate many of the features introduced in the past year very much, and I use a lot of them (the new keyboard shortcuts are my favorite for managing writes to termbases properly when I work), but I might trade it all if I can just be sure again that the software won't crash and burn me just before a translation is to be sent to a client.

I am confident that these matters will be addressed, but the matter of when is of critical importance for many. Issues of novelty versus reliability are hardly new in the software world; I have seen every variation of this for about 40 years now, and Kilgray generally, though not always, earns better marks than the competition. And even with the troubles that have beset memoQ 2015 (which have made it very desirable to keep older versions installed too) it is still the best - and most reliable - tool I have seen for the ways I work.

May 27, 2016

Comments about FIT’s position statement on crowdsourcing (guest post)

A response by Attila Piróth to a recent statement published on the web site of the Fédération Internationale des Traducteurs [1].
Crowdsourcing is certainly a very effective term; calling some of the practices it enables “digitally distributed sweatshop labor” – for this seems like a much better description of what’s happening on crowdsource-for-money platforms like Amazon’s Mechanical Turk – wouldn’t accomplish half as much.
(Evgeny Morozov)[2]
Digitally connected mobs will perform more and more services in a collective volunteer basis, from medicine to solving crimes, until all jobs are done that way.
(Jaron Lanier)[3]

In the past few years, crowdsourced translation and machine translation have received a great deal of attention. Both are frequently called “disruptive technologies”, and are claimed to drive growth for businesses. Professional translators are often advised to get used to the idea that machine translation and crowdsourcing are “here to stay” and to adapt “to the changing landscape of the profession”. Machine translation post-editing is frequently cited as a new “niche” for translators.

The topic choice for the two FIT position statements thus reflects important and interesting realities. However, in its stated role as the “voice of translators worldwide”, FIT should not shy away from discussing some crucial issues that go beyond the simple technicalities presented in the paper. And if FIT is to reasonably call its paper a statement of position, it should dare to state one.

Finding a consensus on the more contradictory aspects will not be easy within FIT. The socio-economic issues that lie at the heart of the heated debates around crowdsourcing and machine translation boil down to the conflict between value creation by independent professionals and value extraction by those who own certain technologies (e.g., MT), linguistic resources (e.g., TMs) or platforms. Once again, we are faced with the labor versus capital debate – which is perhaps one reason why corporate interests like to use the term translation industry. Effectively, crowdsourcing and machine translation aim to ensure the necessary ingredients for the industrialization of an intellectual activity, and (by redefining expectations) to propose alternatives for the scarcity of the required competences. This is precisely why both trends have attracted major capital investments.
Example: Duolingo is a language-learning website that received 15 million dollars of capital funding at an early stage of its development. The core idea as represented to students was to teach languages through translation exercises. The more advanced the learner, the more difficult the sentences to translate. Peer-to-peer voting provides feedback on the participants’ performance. Courses are free, because the core idea as represented to financial backers is that the company generates its income by selling the translations produced by the crowd. The patchwork translations thus provided were meant to be sold to major content creation hubs – gawker, huffpost, etc. This “disruptive” model would thus enable the translation of a huge amount of text (for which “there would have been no traditional budget”). If one consults individual professionals such as language teachers and journalists, they will also add that this platform creates competition not just for translators but for them too – thereby disrupting several professions at once.
This model gives a clear translation-related example to the main thesis of Douglas Rushkoff’s new book, Throwing rocks at the Google bus: how growth became the enemy of prosperity.[4] Crowdsourcing does not enable a sustainable professional career for those who perform it: crowdsourcing is fundamentally a winner-takes-all scheme, in which the only real winner possible is the entity that owns or controls the platform. As the casino business knows, the house always wins.[5]

In the introductory quote, Evgeny Morozov calls crowdsourcing “digitally distributed sweatshop labor”. Given that recent reforms to the French labor law have lead to massive protests, this is also an opportune moment to assess the sort of legislative treatment this digitally distributed sweatshop labor receives.

The short answer is: it is entirely overlooked. Crowdsourcing’s diffusely distributed nature – it is literally everywhere and nowhere – seems to cast an impenetrable veil obscuring links to any physical jurisdiction.

Consider a brick-and-mortar bookstore, which, to increase its profit, invites volunteers to unload the delivery trucks, fill the shelves, clean the floor, etc. The volunteers bear their own costs and have no protection with regard to health, safety, work hours and insurance; they contribute because they identify in some way with the company and its products, and may hope to be offered some kind of paid work eventually.[6] In most countries, that has long been against the law: the company should hire the workforce, pay them at least the minimum wage, pay the various contributions/taxes after the employees, etc. When a company makes a profit, workers are paid, and the state also gets a share in the form of taxes and other contributions.

Over the past several years, many brick-and-mortar bookstores have been driven out of business by a virtual bookstore that has developed one of the most sophisticated platforms in the world: Amazon. As explained in Wikinomics by D. Tapscott and A.D. Williams,[7] hundreds of thousands of volunteer programmers participated in the “collaborative effort” to build the Amazon platform – which debuted as a bookstore, then added consumer electronics (bankrupting Circuit City and Best Buy), and only continues to grow and diversify.

Since the boom of the digital knowledge economy, numerous volunteer “community” projects have been launched under the banner of “harnessing the unused intellectual capacity of the community (the cognitive surplus[8]) for the benefit of all”. But who will extract that “cognitive surplus”? Will the resource extraction models developed in the 20th century for oil, gas, minerals etc. be followed – with notional “competitors” forming close alliances behind the scenes to control ownership of the resources? Cognitive surplus may be even more attractive to mine than physical resources because there is no sovereign owner and there are no cross-border issues requiring negotiations, contracts, royalties or trade agreements. But are nations really OK with having their workers deliver free, untaxable labor to, among others, private foreign interests?[9]

A typical example is when major IT companies can slash customer support costs because an enthusiastic user community is at their disposal to provide peer-to-peer help for free. IT giants like Google, Microsoft, Amazon, Symantec, etc. all benefit from such volunteer help. For these companies, the potential to use unpaid labor in handsomely paid (or even publicly subsidized) projects is not some kind of unexpected but fortuitous glitch: it is a system feature by design.

A perfect example along these lines is the ACCEPT project, in which crowdsourcing meets machine translation. Through this project, the EU generously offered a million-euro check to US digital media companies Symantec and Acrolinx and French translation company Lexcelera to cover some of their machine translation R&D costs. One of the promises these companies made was to scale up the volunteer operations of Translators without Borders (TwB), a nonprofit organization that they control,[10]  and whose actual work is completed by unpaid contributors sourced from all over the world. Thus, although the charitable efforts of the volunteers constitute the most publicly visible aspect of this apparatus, certain companies represented at the top of the hierarchy also benefit much less visibly by deriving privatized profit from free socialized labor.

In a remarkable article published over five years ago, the Northern California Translators Association (NCTA) unveiled the real character of crowdsourcing. That analysis – and hopefully the present one, too – shows that the translation profession is not isolated: it is as strongly affected by social (media) trends as any other profession where telework has become the norm. Legislation lags seriously behind technology, and representative bodies of freelancers must act to close that gap.

A “position statement” by an international federation of professional associations can be a good step in that direction – but as noted at the outset, such a paper will accomplish little if it fails to take a clear position.

Professional associations whose member base is comprised solely of individual professionals are in a much clearer situation than those associations in the FIT family that also admit corporate members. The former should accordingly step forward and raise the issues that are omitted from the FIT paper and negatively affect their members. Raising these critical questions may ultimately mean that no FIT-wide consensus can be reached about crowdsourcing (or machine translation). But that is a much healthier outcome than remaining a silent signatory to the current position statement – and hence tacitly agreeing that there is nothing to see here and we should all move along.

Attila Piróth

Acknowledgement: Some ideas presented above have emerged or crystallized in conversations with colleagues, in particular with Vivian J. Stevenson, who also read the manuscript.

Notes:
[1] http://www.fit-ift.org/?page_id=4355.
[2] Evgeny Morozov, To save everything, click here. Penguin, 2013. ISBN: 978-0241957707.
[3] Jaron Lanier, You are not a gadget. Vintage, 2011. ISBN: 978-0307389978.
[4] Douglas Rushkoff, Throwing rocks at the Google bus: how growth became the enemy of prosperity. Portfolio, 2016. ISBN: 978-1617230172.
[5] “The bigger, centralized solutions offered by corporations with traditional, extractive, and monopolistic strategies are more attractive to investors, who are themselves betting on winner-takes-all outcomes.” D. Rushkoff, ibid.
[6] Interestingly, this kind of effort looks similar to sweat equity. According to Investopedia, “Sweat equity is contribution to a project or enterprise in the form of effort and toil. Sweat equity is the ownership interest, or increase in value, that is created as a direct result of hard work by the owner(s)…” The difference is that with unpaid crowdsourcing, the owners get the equity increase while the crowd contributes the sweat for free with no guaranteed return. Appearing on Stephen Colbert’s talk show in March 2014, Jaron Lanier gave a brief overview of his book, Who owns the future (Simon & Schuster, 2013, ISBN: 978-1451654967), and noted that “…we talked ourselves into this weird double economy, where if it’s about stuff, we believe in markets, if it’s about information, then we think it should be shared, it should be open...”. He also outlined a possibility of how those who contribute to the improvement of Google Translate could be rewarded through a micropayment system that logs the reuse of individual contributions.
[7] Don Tapscott, Anthony D. Williams, Wikinomics: How Mass Collaboration Changes Everything. Portfolio, 2006. ISBN: 978-1591841380.
[8] See for example Shirky, Clay, Cognitive Surplus. Penguin, 2010. ISBN: 978-1594202537.
[9] This is especially interesting in view of the various tax minimization strategies that have also proliferated with globalism. Many of the same corporations that stand to benefit from a given nation’s cognitive surplus can sell back into the same population while enjoying minimal exposure to the domestic tax system. While all this is legal, it nonetheless poses a clear potential strain on any national economy.
[10] For a detailed criticism of the ACCEPT project and the conflict of interest in Translators without Borders’ board, see this post.