There are 164 posts tagged translation (this is page 4 of 14).
There is no denying it, social media and its impact on the way we interact on the web is a growing presence. As consumers, we regularly take to social media to share our lives and connect with others. Whether for praise or to complain, we want our voices heard. Life science companies are no exception. Social media allows businesses to maximize visibility amongst consumers, as well as harness data that is emerging from users worldwide.
Hailed as the Uber of translation services (for reasons that will become obvious shortly), Stepes is a new and revolutionary translation platform that any company or individual can use to immediately connect with 50,000+ translators based in 6 different continents.
A lot of companies have employees who can speak foreign languages to help review content after it’s been translated by the company’s translation provider, or LSP (Language Service Provider). If this is the case for your company, here are 4 tips to keep in mind.
What do Wordpress, Linux, and Firefox all have in common? All of these successful projects are the result of crowdsourced contributions. As the Internet continues to connect us, collaborating on projects has become easier than ever. Even in the localization industry, crowdsourced translation solutions are helping to make translation services available to everyone and the advent of new technologies has brought about a few different methods of collaborative translation projects. Let’s take a look at three of the most popular models of crowdsourced translations.
If you’re not confident in your English but still want to read the New York Times or the Economist and catch up on your sports updates, one of the best ways to stay on top of the news is to visit community-based media sites. Sites such as Facebook, Yeeyan, Hupu, Guokr, and so on, are translated into other languages by everyday Internet users just like you and me.
Crowdsourcing translation offers many benefits to both linguists and companies alike. Yet, like all localization projects, it is important to implement careful strategies for maintaining accuracy throughout the process. In many industries, a small translation error can have a huge impact. Be it a life or death situation, your company’s reputation, or the difference between success and failure in a local market, it is always important to work with a language services provider who understands your content, industry, and brand strategy.
When starting a translation project, clients and language service providers are always searching for the most creative, cost-effective, and efficient way to deliver high-quality translations, and businesses would need to consider the pros and cons for a variety of different providers…
Advances in technology are constantly changing the way we live and, when it comes to translation, this is no different. Gone are the days of flipping through an old ‘Spanish to English’ dictionary, looking up every word in a sentence, and trying to form something that vaguely resembles a translation. Now it is much more common practice to just copy & paste the sentence into your favored machine translation service and instantly receive a more accurate and efficient result. These developments will continue to drastically change the future of translation, not just due to increasing levels of technology but also, through the emergence of platforms in which bilinguals around the world can contribute freely by translating pieces of text – crowdsourcing.
Translation Memory (TM) is a database that stores language “segments” (phrases, clauses, headings, etc.) that you or your company has translated. As you continue translating documents, the database offers up translation suggestions based on those stored segments.
Translation memory is an important tool in the modern translator’s toolkit, and one that is currently the focus of a great deal of discussion in the translation and localization community. Simply put, translation memory is a type of shared database that stores translations and continually updates itself as its users work.
