Over the past 24 months, we’ve seen a number of interesting developments in the world of machine translation (MT). Now, with the technology behind MT improving rapidly, the popularity of multilingual machine translation engines has skyrocketed. As a result, you may find yourself in a situation thinking whether MT is the right solution for your translation and localization strategy.
To help you decide whether this is the case, we’ve created this introduction to machine translation. We’ll provide an overview of what MT is, the approaches available and the accuracy of machine translations.
What is MT?
According to the definition of machine translation from the ISO, MT involves the ‘automatic translation of text from one natural language to another using a computer application’.
However, although MT can be used to translate texts without human input entirely, it’s often used alongside human translators who provide post-editing services.
What MT approaches are available?
As the technology behind machine translation has progressed, new approaches have become available. These include:
- Rule-based machine translation (RBMT) – This is the forefather of MT and the approach is based on grammatical and syntactical rules. However, it’s now largely obsolete.
- Statistical MT (SMT) – This method uses large volumes of existing translated texts and statistical models to generate translations. However, it’s quickly being overshadowed by other approaches because it’s time and resource intensive.
- Neural MT (NMT) – This is a newer approach that is built on deep neural networks. It generally creates translations that are more fluent and grammatically accurate. However, it struggles translating rare words and terminology.
In addition to this, when it comes to machine translation engines, it’s important to say that there’s a difference between generic MT engines and custom MT engines. While generic MT engines like Google Translate and Microsoft Translator are not trained with data for a specific domain or topic, custom MT engines like Google AutoML are finely tuned because they’re trained with specific data. This means custom MT engines create a more accurate output than generic MT engines. However, Custom engines incur in additional costs, that are linked to their learning capabilities.
Is machine translation right for my business?
MT meets the needs of an increasing number of businesses that require large volumes of content to be translated within a tight timeframe or at a vastly reduced cost. After all, machine translation is fast, can translate content into multiple languages at once and can help free up the time of translators.
However, multilingual machine translation systems still cannot produce translations that are as accurate as those created by human translators. In addition, despite technological advances, computers can’t always understand context and culture. Although machines can translate content without human input, many of these translations require post-editing to determine their accuracy.
If you have a large volume of content that needs translating in a short period of time, MT is likely to be very effective. If accuracy isn’t vital, then you may also not require human input. However, the more complex and nuanced your content is, the less suitable it is for MT, and post-editing will be required to ensure machine translation accuracy.
As a result, if you’re currently using machine translation for your localization projects, speak to us about our MT services. We can ensure that your machine translations are accurate and culturally relevant. Alternatively, we can also provide machine translation accuracy testing through our consultancy services. Simply contact us today to learn more.
Enifa moved to the UK from Macedonia in 2011 before completing her postgraduate degree in multilingual information management at The University of Sheffield. After graduating she took on her first translation project management role and very quickly became skilled in handling complex web localisation projects across a variety of sectors. Her problem-solving approach to project management has enabled her to develop close relationships with clients by trying to find the most cost-effective and efficient approaches possible. She is a very quick learner and an incredible teacher. She is responsible for finding and implementing training that will benefit the overall project management team, as well as training new starters.