Surviving crowdsourcing

IALB-ASTTI XXXIV Annual Conference Geneva, 13 November 2009 Community, collaborative, social. Will the language industry survive crowdsourcing? We cannot solve problems by using the same kind of thinking we used when we created them. Albert Einstein Despite the unchanged and even increasing demand for translation, the economic crisis and the consequent turmoil have spread their effects on the language service industry too with lower and lower pays and worse conditions, and a deep change is approaching. The approach to crises is usually of two kinds, conservative or innovative. The conservative approach is typical of the language services industry, where any change is seen as a major change. Companies resorting to the conservative approach to face crises usually go for cutting costs. Since reducing staff is impractical in an industry almost entirely based on outsourcing and freelancing, cutting costs is pursued through lowering pays and using but not investing in technology. Unfortunately, as a recent Common Sense Advisory report showed, the translation industry is generally a low-tech industry where do more with less is an imperative that very few can follow. The innovative approach to crises relies on products. However, since very small product innovation is possible in a century-old activity, this could happen only with the so-called value-added services. This could explain in part why many still look at translation as a cost, and the consequent competition on prices. Therefore, only process innovation can be effective, but for process innovation standards and interchange are needed to build productive solutions. Developing standards, however, is not enough per se; standards should be used, and industry fragmentation retards or even impedes the adoption of standards. Again, technology is used to make process simpler, lean, and parallel, while being open, easy, lean, agile is the only way to face the new translation industry mantra of “cheaper, faster, and better”. Doing more with less costs more, and technology is not enough; technology can help increase volume and speed, but it is almost useless to streamline processes. Processes pivot on people, and there are not enough good people in translation. The demand for translation is increasing; content is doubling every year, while the number of translators can’t follow the same growth rate: it takes many years to create a professional translator. The only way to approach this growth in content is by increasing translators’ productivity, but education and training play different roles in building a translator’s profile, and language skills and domain-specific knowledge should be kept separate from productivity skills. In fact, technology alone can produce only poor quality increase or no increase at all, and customer satisfaction decreases, together with industry reputation. Translation has traditionally been viewed as a craft, now it’s time to shift to a different approach where highly skilled individuals are no longer enough to perform increasingly composite jobs. Translation is a business and should finally be considered as such. Translators must speak money and tune their business on their clients’ business. Translation is increasingly bound to money and time, while quality has also definitely lost the meaning it has had for long in translation studies. Quality is doing it right the first time. This means having efficient processes in place and first-class people at work, as everyone could translate but few can do it right. To keep pace with the changing situation, the translation process must be rearranged. As it was for the software industry a few years ago, “agile” should become the new buzzword of the translation industry. On the contrary, the translation industry is still tied to the axioms of quality and assets. Quality is the unique selling proposition of the whole industry, making offers indistinguishable, whereas, to customers, quality is a prerequisite, a condition of existence, and is hence totally irrelevant from a sales perspective, especially the way it is postulated and talked about in the translation industry, because it is based on the premise that customers can assess and appreciate it. For quality to make sense it must be backed up by proof. No quality exists until it cannot be defined and measured. © Luigi Muzii 2009, Some rights reserved 1

IALB-ASTTI XXXIV Annual Conference Geneva, 13 November 2009 The quality axiom goes together with the corollary that fewer translators produce more consistent output, as if a reader could distinguish some ten thousand words in a million. This is used to justify the asset axiom, where assets are glossaries and translation memories. Assets are supposed to carry some value, but glossaries and translation memories not necessarily carry and intrinsic value, since in this case value comes from their exploitation, and this depends on the translator’s ability. Hence, not only are highly skilled individuals no longer enough, technology too is no longer enough. A close combination of the two with a new process model could be an answer. Nevertheless, the main concerns of LSP’s are for the ongoing commoditization and the demand for more free translation. In the ATA Chronicle issue of June 2009, ATA President Jiri Stejskal reported the globalization threats identified by the ATA Board in global outsourcing, crowdsourcing, the economic downturn, certification by other entities, machine translation, the increased competition for revenue streams, the international expansion, and licensure. Vendor management is the largest cost budget item, and could be huge for companies with hundreds or thousands vendors. Vendor management requires dedicated technology and staff, and involves delicate tasks like quality assessment. To keep healthy relationships with vendors, several vendor managers could be necessary to rotate. Few concepts in business have been as popular and appealing in recent years as the emerging discipline of “open innovation.” It is variously described as crowdsourcing, the wisdom of crowds, collective intelligence and peer production. Low cost electronic communication enabled by the Internet now makes it feasible for crowds to do many more things than ever before. Peer production models leverage the connected-but-distributed nature of the internet to bring broad human resources to bear on specific tasks or problems. Wikipedia stands as the flagship example of this. Collective intelligence can be defined very broadly as groups of individuals doing things collectively that seem intelligent. By this definition, collective intelligence has existed for a very long time, even in the translation industry. However, recent cases and research of the Center for Collective Intelligence at the Massachusetts Institute of Technology suggest that open-innovation models succeed only when carefully designed for a particular task, and when incentives are tailored to attract the most effective collaborators who should be paid for their work.. The reasons for crowdsourcing are reaching totally new markets, better serving markets that are currently under-served, and increasing the value of global brands by further engaging users as a competitive strategy, as for the Facebook translation model. This compelling engagement strategy requires the entire user experience to be in the user’s language. This is the reason for Facebook, for example, to be available in almost 100 languages, and the reason for translation crowdsourcing. Yet, the Facebook help is available in less than 10 languages: according to Facebook itself, if chunks to be translated by the crowd are bigger than two lines there are no takers. The typical practice in translation has been the same for centuries. The appearance of commercial translation approximately a hundred years ago gave birth to the industry as we now know it. The translation industry has always been almost entirely based on outsourcing and freelancing, and vendors are the necessary link between customers and translators. Similarly, fragmentation has always been a typical trait, with vendors duplicating the same tasks at each stage before reaching the last link in the production chain, in a top-down serial process, going backwards when the translation is ready. Consequently, the TEP (Translation, Editing, and Proofreading) model has been central to the translation workflow: a translator does the job, somebody else (possibly another translator) reviews the job, and then a final check must be performed to have quality. In a post of 16 October 2007 for Common Sense Advisory’s blog Global Watchtower, Renato Beninatto and Don DePalma envisaged “The End of Localization Taylorism”. TEP is connected to the standard, axiomatic, view of quality. The translation industry developed the concept of quality with the idea that every translation is worked on and looked at by three different sets of eyes, even though smaller translation teams might not have or afford such a plenty of resources. As a matter of fact, the traditional translation process does not work well for every type of project, and yet, even in the translation industry, fordism has prevailed: any effort is committed to make everybody do the same, not necessarily the best job. The best job is meeting the client’s expectations — which include timely delivery and fair price — while making a profit. The TEP philosophy is based on the idea that quality comes from catching errors. The reviewer’s job is to catch errors that the translator (supposedly) made, and the proofreader’s job is to catch errors that the editor didn’t see. The problem with this process is consecutiveness; it is a chain/serial process, while in a parallel process quality can be improved by adding actions. © Luigi Muzii 2009, Some rights reserved 2

IALB-ASTTI XXXIV Annual Conference Geneva, 13 November 2009 Excellent translators know how to prevent errors using a style guide and a glossary rather than just catch them once asked to review a translation. Excellent translators need no reviewers, they need knowledgeable support. Translation crowdsourcing is simply a different way of selecting, collecting, and connecting the people to perform the roles in the various stages of the workflow. Crowdsourcing is “the new black” these days, but, despite the trend, companies should be very careful before embracing the model. Few of them are translation worthy, and there is always the risk of translator backlash or burnout. It is mostly an unexplored territory, and as more companies pursue this model, more and more efforts will backfire. Translation crowdsourcing is not about saving money. In a collaborative translation project following a typical crowdsourcing approach only the best translators in the project subject field are engaged, to work on their most productive side. Translation crowdsourcing is simply faster. Whoever has worked with contracting services for a specific business knows how long and painful the process can be. Translation crowdsourcing can bring results in a fraction of the standard time. Yet, a translation crowdsourcing platform costs money to put in place. In most cases, the initial investment is likely to be higher than the cost of one-off professional translation services, and the ROI needs to be there for the long-term to justify it. There is possibly a problem with translators when a profitable company goes for building a community and paying engineers to set up a complex software infrastructure rather than hiring a translation company for a cheaper one-off translation project. Maybe translators’ skills are simply not worth spending, or, once it is up and running, the translation platform can be expanded to help developers localize their applications, leased, or opened up to “partner” sites, etc. Twitter, for example, has only a thousand or so text strings requiring translation. In the time the company devoted to building its translation platform, it could probably have had the site localized in fifty or more languages. Over time there probably will be cost savings, but they should not be and probably were not the motivator for Twitter. The top-down localization model is giving way to the bottom-up model, and this is a profound change, even if it is limited to a handful of companies, maybe with a few hundred million users. The collaborative model makes reviewers no longer necessary. We learned from translation studies that there could be several “correct” translations. Why should experienced and skilled translators, possibly assisted by qualified subject matter experts not be capable of delivering high-quality translations without the necessity of further improvements by a reviewer? The typical collaborative translation team is made up of a pool of subject matter experts and translators led by a project manager to manage the flow of information among team members. One of the more time-consuming and unnecessary problems in communicating with clients is for disagreements on supposedly wrong — maybe better imperfect — translations. Typically these disagreements come once the project is finished and often require a lot of effort on both sides to fix. They do not concern errors, but simply stylistics or other preferences. In the collaborative model the whole cast concurrently work on a project. The model is inspired to the scrum iterative incremental framework, a holistic approach to increase speed and flexibility in software development. Scrum enables the creation of self-organizing teams by encouraging co-location of all team members, and communication across all team members and disciplines that are involved in the project. In the collaborative translation model, projects revolve around communities that come together for each project. It is a distributed model based on the concept of “participation” as the defining feature. In a collaborative translation project, the project manager is actually a “facilitator” whose job is to create a “community” with the translators, a subject matter expert to answer questions about the topic, all the tools to run the project, and to assure support to translators where they need help. A few consultants will also be hired to handle the shared translation memories, the term base, develop the style guides, and tune the machine translation engine. In a collaborative translation project there is no need for reviewers and editors that could be disruptive for harmony and confidence in the project team, while the project “facilitator” and the consultant will do their best to have translators strictly follow the style guide and glossary. The collaborative model challenges the traditional, aged TEP approach to translation and translation quality. On its website, the U.S. company Mojofiti offers a comparison of crowdsourcing and machine translation through their pros and cons. Crowdsourcing is free, with abundance of knowledgeable human translators, 100% accuracy, and demonstrates openness to the public, while security measures must be in place, content accuracy reviews are needed (especially from a cultural perspective), and no full-time recruits can be staffed. Machine Translation allows for fast time to market, servers can be used for a variety of applications, and content can be changed ad lib. On the other hand, it is generally very costly (based on the number of language pairs and sometimes overall use), and often leaves out cultural © Luigi Muzii 2009, Some rights reserved 3

IALB-ASTTI XXXIV Annual Conference Geneva, 13 November 2009 references; in addition, no machine translation service has been able to promise 100% accurate translations, yet. Nothing precludes inherently quality, at least as it is conceived in the translation industry, in a translation crowdsourcing platform, as long as crowdsourcees are professional translators. The wisdom of the crowd comes from the cross check of each other’s work. In fact, crowdsourcing initiatives can be dangerous when cost is volume based, and since money matters compensation is a critical issue. Bad money would drive out good. If translators are paid low hiring qualified professionals will be harder and harder. Anyway, not only does a collaborative translation platform require resources, even the management of the platform and the management of the team members require resources. Any savings should be used to pay the team members, not only to make profits, and retain professional, knowledgeable, skilled, and experienced translators. In general, LSP’s share the same pool of resources, and testing freelancers is expensive and not reliable. Then, the solution to reduce overhead is shortening the production chain. The next step in translation technology will be collaborative (crowdsourcing) platforms combining workflow and computer-aided translation capabilities into one application. Humans can translate text of any kind, rank the translation for accuracy and provide final edits, all via a browser from anywhere in the world. Machine translation will capture translated text and then suggest the translated text when the words or phrases appear again, reducing the overall time and effort required for translation. No overhead for administrative activities, no duplication of work. Therefore, a new scenario could dawn soon: translators organized in large pools with a certain amount of work and income guaranteed every month provided that they check in on a regular basis. Technology is running fast, even translation technology, although no winning translation technology has come from the translation industry so far: all disruptive innovations in translation have come from the outside. Social networking sites were not the first to resort to crowdsourcing, the software industry has been crowdsourcing for years. When translation tools first hit the market, the majority of translators received them as a threat, crying hysterically at the very poor quality these tools would surely produce. Almost twenty years later, the scene has not changed. As a matter of fact, the hostility of translators led translation tools to meet the requirements of paying corporate customers, not those of translators. Should they have embraced the technology from the very beginning, the landscape would be different now. In a May 2009 survey, Common Sense Advisory estimates over 20,000 translation service providers, and more than 300,000 individuals earning all or part of their living in translation business. The survey also showed that many translation professionals employ less automation than they could and should. Translators are conservative, so they will be slow to accept cloud-based computing and the collaborative approach. Crowdsourcing will greatly depend on the nature of the business. Pro bono work is usually done for a cause. The “free” translation crowdsourcing caused an uprising against LinkedIn with most insurgent convincingly arguing that they would not donate their time and expertise to a business which will generate revenue out of their free contribution, and that the LinkedIn call was not fair. This does not mean, however, that crowdsourcing is an unethical practice. The negative international media coverage made LinkedIn cancel the project altogether more than ATA’s official stance against LinkedIn’s translation crowdsourcing project — “misguided, troubling, and clearly incompatible with the operation of a for-profit enterprise.” Freeconomics is the new frontier in translation industry, at least for having introduced freemium (a portmanteau of “free” and “premium”) a long time ago. In fact, test translations are a kind of freemium, like shareware. Translation is gradually shifting to online economy, and the costs associated with online economy are trending toward zero at an incredible rate. The translation industry, and translators first, should rapidly adapt to freemium, offering basic services for free, while charging a premium for advanced or special features. Translation crowdsourcing is here to stay. It will combine rather than compete with machine translation and professional human translation and will find its niche. The collaborative translation model will eventually change also the language service industry, and the problem is in transition: crowdsourcing will prevail; is it convenient to hamper the power of the crowds? In 2006, in suggesting the Edge annual question for the Edge Foundation, paraphrasing Oscar Wilde’s “an idea that is not dangerous is unworthy of being called an idea at all ”, cognitive scientist Steven Pinker wrote: «the history of science is replete with discoveries that were considered socially, morally, or emotionally dangerous in their time». What is your dangerous idea? © Luigi Muzii 2009, Some rights reserved 4

IALB-ASTTI XXXIV Annual Conference Geneva, 13 November 2009 References Beninatto R. & DePalma D., Collaborative Translation, Common Sense Advisory, 2007, ISBN 978-1-933555-48-5 (Pending) Anderson C., Free: The Future of a Radical Price, Hyperion, 2009, ISBN 978-1401322908 Brockman J., What Is Your Dangerous Idea?: Today's Leading Thinkers on the Unthinkable, Harper Perennial, 2007, ISBN 978-0061214950 DePalma D. A. & Sargent B. B., The State of Freelance Translation, Common Sense Advisory, 2009, ISBN 978-1-933555-66-9 Howe J., Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, Crown Business, 2008, ISBN 0307396207 © Luigi Muzii 2009, Some rights reserved 5

Surviving crowdsourcing

by Luigi Muzii

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