by Bill Swallow in Scriptorium / 01.2018
Machine translation continues to evolve. With artificial intelligence in the mix, machine translations seem almost human. Google Translate is one of the top players in this market, supplying everything from basic text translation to browser-embedded (Chrome) translation to a robust translation API. But even with all of these options, is Google Translate good enough?
Google Translate and other machine translators provide translations almost instantaneously and with high accuracy rates. But are they good enough?
Successful translation, machine-generated or otherwise, still depends on three core content facets: audience, subject matter, and quality.
Audience expectations matter. Do your audiences expect content tailored to them, or are they accepting of potential translation flaws?
Your audience may be more forgiving of translation errors or awkward phrasing in general content. They may be less forgiving (or insulted) if instructional or targeted content contains obvious flaws.
Regarding Google Translate, using their API to directly translate and publish web content might be good enough. But if your audience expects tailored content, additional steps are needed in the translation process to ensure the content speaks to them.
Know your audiences’ expectations before deciding on your translation approach.
The subject matter of your content also affects machine translation accuracy. Will machine translation tools understand your terminology?
Machine translation tools are only as smart as they are trained to be. If you use special terminology, or if your content is highly technical and must be absolutely accurate (such as with life sciences), you must train the tool to understand this content.
Use a corpus of your content to train the tool how to appropriately translate your content into other languages. This process is neither quick nor easy, but is essential for accurate machine translation.
Content quality is always important, but it is critical in the case of machine translation. Are you building quality control into source content development? Do you strictly enforce style, terminology use, and content structure?
Machine translation is most successful with content that is consistently written, using specific terms and phrases in specific contexts. Frequent use of synonyms in varying contexts can confuse machine translation tools. Likewise, inconsistent content structure can result in different translations for similar strings of text.
Slang, colloquialisms, and jargon are also problematic. The machine translation tool must be trained to understand these constructs and equate them to culturally appropriate localized phrases. If not, the translations will be literal, which could be embarrassing or downright offensive.
If stylistic inconsistencies, slang, colloquialisms and such cannot be avoided, consider using human translators and employ transcreation where culturally appropriate translations of more colorful phrasings are needed.
Yes and no. It really depends on how you intend to use it, and whether potential inaccuracies are allowable.
If your audience does not need or expect a 100% accurate translation, then Google Translate may be good enough. If accuracy is critical, you may need to look at other translation options, or at least build in a thorough review process and custom corpus maintenance into your use of NMT.