The Language Industry and the Triangle Project
If you work in the language industry and have not heard of crowdsourcing translation, you’re behind the times! Crowdsourcing is everywhere. Do you use Wikipedia? Then you’re a consumer of crowdsourcing. Do you buy photos from iStock Photo for a project? That’s right, you’re using crowdsourcing. Now consider the dominant language service model: a global network of skilled linguists hired to translate specific pairs of languages and content. This is just one professional variation of the same concept.
Exactly, crowdsourcing is in the DNA of our industry and it came to stay. What will be the role of crowdsourcing translation in the language industry? Has the future of crowdsourcing already arrived? And, how can we use crowdsourcing translation to improve our ability to serve our customers best?
First, we need a definition for crowdsourcing and there is no better place to find one than the most used crowdsourcing tool in the world, Wikipedia: “Crowdsourcing is a specific sourcing mode in which individuals or organizations use contributions from Internet users to obtain needed services or ideas.“
Now, let’s get down to basics. Who does not know the triangle project? We know that there are three variables – quality, time and cost – that always define the project, regardless of size or scale. And we always look for these variables to match: good quality, fast time and cheap cost. However, you can only choose two – the triangle concept reflects the idea that these three variables are always interrelated, that it’s not possible, or, even, desirable, to try to optimize all three. One will always suffer.
The crowdsourcing translation model currently used in the language industry is focused on time and cost. In other words, quick time and cheap cost. The orphan variable is quality. As this model is relatively new and is still in its initial phase, the triangle project tied to it is beginning to be tested by the demands of the market.
What can we do to reinforce the orphan variable (quality)? As professionals in the language industry, we bring a wide range of practices and tools to the art and science of translation.
Below we suggest some practices that we believe are essential to deal with this new translation method.
Fundamental to the success of any project, regardless of type and industry, is the identification of roles, responsibilities, actions, schedules, steps, goals and objectives. Especially when it comes to crowdsourcing translation projects, planning should be done early and often.
The approach taken by Amazon’s Mechanical Turk, for example, to organize and deconstruct project tasks in so-called Human Intelligence Tasks or HITs, reflects the need for a rigorous systematic framework to help organize the work process. This deconstruction of projects into small, single tasks is the first step toward a systemic approach that involves network crowdsourcing capabilities.
Applying rigorous standards and assessments that ensure that skills and expertise are assigned to the project is always a critical aspect of any translation project. Today, there are three types of translation available: professional (i.e. global networks of professional linguists), amateurs (e.g., untrained translators) and automatic (specialized software to translate content). Quality definitions depend largely on the translation provider selected for a given project. With the progress of the language industry, we will see an increased integration of these three types of translation, thus requiring even greater attention to standards and assessments to ensure quality especially when it comes to crowdsourcing translation.
Use of TM
The continuous and refined use of translation memory (TM), as well as the management of glossaries and terminology, has contributed significantly to successful crowdsourcing translation projects. In this context, the evolution of TM to a phenomenon of cloud-based crowdsourcing demands special attention.
The icing on the cake of language solution by crowdsourcing. The ability of language providers to adapt and develop quality control procedures that align with the dynamic nature of content translated by crowdsourcing will be a key element. And the more the qualification decisions (professionals x amateurs) and the quality requirements (high, medium, low) are counterbalanced, the more critical this element becomes.
In conclusion, the use of crowdsourcing translation in the language industry will not come to an end. Without a doubt, it will evolve driven by market demands for greater integration and ease of use, and by developers working continuously to harness the collective capabilities of people around the world on a global network.