Abstract
Collaborative translation has the potential for significantly changing how we translate content. However, successful deployment of this kind of approach is far from trivial, as it presents potential adopters with a rich and complex envelope of processes and technologies, whose respective impacts are still poorly understood. The present paper aims at facilitating this kind of decision making, by describing and cataloguing current best-practices in collaborative translation. More precisely, we present a collection of Design Patterns which was created collectively by a small group of practitioners, at a one-day roundtable hosted by the Translation Automation Users Society in October of 2011. This collection has been put on an open wiki site (www.collaborative-translation-patterns.com) in the hopes that other practitioners in the field will refine and augment it.
Highlights
Collaborative and social networking technologies, as seen on sites like Wikipedia, Facebook and Amazon Mechanical Turk, are having profound effects in many spheres of human activity
Translation crowdsourcing conjured up a picture where customers could get fast translations at rock-bottom prices, through the work of volunteer or offshore translators and the help of new technology acquired from vendors, while professional translators risked seeing their profit margins shrink drastically
There are many poorly understood issues and open questions regarding the best way to deploy collaborative translation in specific contexts, and this comes out clearly from the proceedings of a recent workshop on that topic held at the 2010 conference of the Association for Machine Translation in the Americas (AMTA) (AMTA, 2010)
Summary
Collaborative and social networking technologies, as seen on sites like Wikipedia, Facebook and Amazon Mechanical Turk, are having profound effects in many spheres of human activity. Translation is no exception, as evidenced by Facebook’s use of crowdsourcing, to co-opt its loyal user base into translating the system’s web interface on a volunteer basis (Ellis, 2009) Using this approach, Facebook was able to rapidly recruit 250,000 volunteers, who translated 350,000 words into 70 languages, often with very short lead time (less than two days for high density languages like French) (Baer, 2010). It brought together several leading practitioners of collaborative translation who worked together to create collaborative-translation-patterns.com, a wiki site that captures some of the most successful best-practices in the field today
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