TAUS Transcreation Best Practices and Guidelines
This document is the first and only best practices and guidelines for transcreation within the translation industry.
The editorial board members Alvaro Villalvilla Merelo (Senior Global Localization Manager, Nike), Angela Petrilli (Translation Regional Manager, Workday), Makiko Aoki (Senior Transcreation Manager, Burberry), Vladimir Zhdanov (Localization Specialist, ex-Alibaba Group) chaired by Paul Mangell (Director, Global Strategic Consultancy, Alpha) came together to discuss and agree on:
- What transcreation really is
- How to differentiate it from other industry terms
- Steps to produce excellence in transcreation
- Importance of cultural relevance
Translators in the Algorithmic Age
Translation is being transformed by the forces of global business and new technology. This means that the human resources used in the industry - typically translators - need to reassess their role in the emerging translation landscape. This report focuses on the role of translators in an environment now driven by data at every level and disrupted by tougher competition, new management priorities, and a concerted effort to use machine learning in business generally. You can also download the no-time-to-read version of the report.
Bridging the Gap between Academia and Business
In this report, we share our findings from an earlier survey carried out among TAUS Academic Members. Topics such as how the academia can benefit more from what TAUS resources have to offer them and how industry and academia can mutually be beneficial in taking the translation industry ahead have been covered.
The Translation Industry in 2022
In this industry report, we share our predictions for the future of the translation industry in line with our expectation that automation will accelerate in the translation sector during the coming 5 years. The anticipated changes will inevitably bring along various challenges and opportunities all of which are explained thoroughly in the Translation Industry in 2022 Report.
TAUS Machine Translation Market Report 2017
MT technology itself is on its way to becoming a commodity, shifting the critical growth factor to language data, i.e. language-pair, speech and monolingual data sets) that are used to train the engines. In this Machine Translation Market Report, we zoom in on the different types of offerings and players in the MT market, with a special focus on the emergence of neural MT.
Quality and the Translator Guidelines
Helping Translators Deliver the Expected Quality. Quality is the most-talked-about subject in the translation and localization industry. Translators are often in the center of the conversation.
This brief report explains:
- The best practices for the translation buyers to follow when assigning a task to translators.
- The best practices for translators follow to deliver the best quality possible.
Quality Evaluation using Adequacy and Fluency Approaches
Adequacy and/or Fluency evaluations are regularly employed for assessing the quality of machine translation. However, they are also useful for evaluation of human and/or computer assisted translation in certain contexts. These methods are less costly and time consuming to implement than an error typology approach and can help to focus on assessing quality attributes that are most relevant for specific content types and purposes.
Readability Evaluation Guidelines
This type of evaluation enables you to assess the readability and comprehensibility of translated content. It's desirable to focus on the quality attribute sentiment and give minimal attention to the quality attribute utility. Time constraints apply and a monolingual evaluation model suffices. Readability evaluation can be integrated within other evaluation models or used on its own at a specific stage in the localization process or used in isolation for particular content types. In the Readablity Evaluation Guidelines, we focus on approaches and good practices for evaluating readability separately from the application of other evaluation approaches.
MT Post-editing Guidelines
An agreed set of post-editing best practices.
Machine translation (MT) with post-editing (PE) is fast becoming a standard practice in our industry. This means that organizations need to be able to easily identify, qualify, train and evaluate post-editors’ performances.
An agreed set of best practices will help the industry fairly and efficiently select the most suitable talent for post-editing work and identify the training opportunities that will help translators and new players, such as crowdsourcing resources, become highly skilled and qualified post-editors.
Last modified: Monday, 15 July 2019, 4:18 AM