Abstract
Crowdsourcing involves the outsourcing of processes previously conducted by professionals in structured ways to communities and crowds using innovative workflows in order to achieve the best possible results. This chapter deals with the way in which the notion of quality has been impacted by the crowdsourcing revolution in translation. After defining the scope of what crowdsourcing is in translational contexts, it delves into the impact of crowdsourcing in terms of how the industry and translation studies conceptualise and implement quality. The main issues reviewed will be the consolidation of process-based approaches to guarantee quality, the expansion of the fitness for purpose model, and the distribution of responsibility to different agents that participate in the translation event. The chapter ends with an exploration of novel practices and workflows to guarantee quality inspired both by professional approaches and by MT research in existing crowdsourcing initiatives.
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Notes
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These are Read-Only Memory files, commonly used for emulation of outdated or incompatible software such as video or arcade games.
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According to the working definition of quality for MQM, “a quality translation demonstrates the accuracy and fluency required for the audience and purpose and complies with all other specifications negotiated between the requester and provider, taking into account end-user needs” (Koby et al. 2014).
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The notion of adequacy here is understood in functionalist terms (Nord 1997) to accept that translations can be more or less adequate for the purposes intended, and not the common use in MT to indicate that the translation is more or less coherent with the meaning of the source text (Papineni et al. 2002), also often used in recent standardisation efforts (i.e. Görög 2014a).
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Defined as a rough translation “to get some essential information about what is in the text and for a user to define whether to translate it in full or not to serve some specific purposes” (Chan 2014).
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More recently Omniscien Technologies (see https://omniscien.com/)
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Jiménez-Crespo, M.A. (2018). Crowdsourcing and Translation Quality: Novel Approaches in the Language Industry and Translation Studies. In: Moorkens, J., Castilho, S., Gaspari, F., Doherty, S. (eds) Translation Quality Assessment. Machine Translation: Technologies and Applications, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-91241-7_4
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