What is it?
A software-based process that translates content from one language to another without human intervention. People may be involved in training software for specific domains or post-editing the output for linguistic quality or style.
Why is it important?
Machine translation accelerates the process, and reduces the cost, of translating content and increases the availability of translated content. Linguistic quality and accuracy levels vary, depending on how well the software is tuned and whether the content is post-edited by humans.
Why does a technical communicator need to know this?
Business professionals should care about machine translation (MT) based on the numbers. Far too much content is being created and far too few translators or money exist to translate it all – or even a small fraction of it – into the dozens of languages that are required to address major global markets.
Translation automation tools, such as MT, promise to increase the volume and accelerate the pace of words rendered into other languages. Understanding this dynamic puts business professionals in a better position to take advantage of what’s happening with this core technology.
Translation strategies that rely on human output alone have already been overwhelmed by the explosion in content and the imperative to rapidly enter new markets. If business professionals are going to meet the needs of their many users, they will have to evaluate how to integrate MT into their global content strategies, regardless of the type or size of their organization[Lommel 2017][Görög 2017].
- [Lommel 2017] Neural MT, Sorting Fact from Fiction: Lommel, Arle (2017). Common Sense Advisory article that discusses how neural machine translation fits in the language technology landscape.
- [Görög 2017] How to Improve Automatic MT Quality Evaluation Metrics: Görög, Attila (2017). A TAUS blog post that discusses how to improve and automate the collection of machine translation quality metrics.