Owing to the current trend of globalization, there have been cases of increased interactions between people and cultures of the world. As the globe feels smaller, the demand for skilled translators is bigger than ever. It’s hard, if not impossible to imagine the globalized world without machine translation engines. Trained engine or not, machine translation always results in a qualitative result, very much similar to what humans can produce. Using machine translation engines smartly can increase the translator’s productivity without compromising on quality. Significant strides have been made in the past decade to improve on the quality of machine translation engines. 

The Adoption of Machine Translation Engines Can Help Overcome Many of The Differences That Language Differences Bring About

Machine-aided translation is undoubtedly one of the most challenging artificial intelligence tasks. That’s because language is constantly changing, evolving, and adapting to the needs of users. With the care and expertise of a human translator, you can count on a higher quality outcome. An ever-increasing number of companies and organizations are turning to machine translation to communicate with individuals in multiple languages. Technology helps translators deal with scarce resources by speeding up their work and allowing them to focus on the essential. While previous iterations of language translation merely achieved the level of gisting, newer translations fluently communicate ideas. 

More often than not, communication involves distant cultures with different languages. Language barriers result in miscomprehension or complete loss in communication. The adoption of machine translation engines can help quickly process and translate content. Thus, it becomes available in other languages and accessible to users worldwide. Attention needs to be paid to the fact that technology isn’t enough to overcome language barriers. Certain projects require extra care and nuance that comes from different grammatical rules, semantics, syntax, and cultural influences. Only a human translator can capture the original meaning and convey that message. 

A Human Translator Still Needs to Review the Existing Translations and Input New Ones 

Improvement in machine translation output depends on human involvement. It’s possible to “train” machine translation engines so as to produce higher-quality translations over time. If you want to know how to train machine translation engine, two solutions are available. You can either make a change in the way you write your input text or make the transition to an adequate translation technology solution. Custom engines are trained using data provided to them to refine the output. Utilizing translation memories is useful because it supports the localization process, improves the quality and consistency of the translation, and helps complete the work faster. 

Not Every Machine Translation Engine Is a Lucrative Investment for Your Content Translation Strategy  

Rule-based machine translation and statistical machine translation used to be the main approaches. At present, the focus is on neural machine translation, which uses an artificial neural network to translate the source text into the target text. It’s highly likely to get a near-perfect segment that requires minimal post-editing. Neural machine translation engines present advanced features. A great deal of time can be saved when translating lengthy text documents, not to mention that it’s possible to memorize key terms and reuse them whenever appropriate. Pangeanic’s neural machine translation engine enables the dynamical translation of user-entered texts, maintaining high levels of security and confidentiality. 

Choosing one translation machine engine over another is a slightly complicated process. It’s recommended to use two or three compatible machine translation engines per language pair if they provide equal results, then it’s a good idea to use the one that is most profitable. Performance depends on information quality, linguistic quality, translation quality, and fitness for the purpose. There’s no perfect machine translation technology. It all comes down to the specific needs of the user when it comes to content type, subject matter, and target market.

Evaluating The Output of Machine Translation Engines  

Evaluation is a way to find out how good or bad the translations produced by a machine translation system are, without human intervention. Similar to experts in their fields, translation solutions are suited for certain types of translations. For instance, some machine translation engines produce good results in terms of translating apps or web pages. It’s, therefore, necessary to define project requirements and quality expectations. from the very get-go. It’s a balancing act that takes place between cost, quality, and time. Whatever the translation needs, there are ways to make it work. The aim of analyzing translation outputs is to obtain relevant insight for further improvement of machine translation quality. 

It’s important to keep in mind that machine-aided translation doesn’t fit all content types and it is not a substitute for human translators. It is a complementary tool. Where accuracy is of the essence, it’s paramount for the human translator to have deep subject matter knowledge. The linguist will take a look at the source and target content to identify issues with accuracy, terminology, and so on. Machine translation doesn’t work best for content types such as marketing copy, which demands a high degree of creativity. It can be used as a starting point, yet it’s not the ideal solution. The point is that the translation approach should be aligned with the content type. 

As mentioned earlier, machine translation engines can be trained for a new project. Before taking action, it’s essential to establish objectives and preferences for tone and voice, terminology, and style for the target languages. The machine translation engine will yield better results, meaning that the translation is what it’s meant to be. Introducing the right machine translation engine for your workflow is an ongoing process that requires repeated testing and training. Machine translation engines can be customized and trained to fit the content strategy and provide the best result post-editing. If you’re struggling to decide what translation machine engine to use, it’s better to use a newer version of the translation solution than its predecessor. 

Machine-aided translation requires a final review, though. Because machine translation can’t capture cultural connotations and doesn’t understand context and tone, it’s necessary to proofread for potential errors. Nobody can afford to hurt relations with grammatically incorrect and culturally disconnected content. That is the next frontier, to make AI software such as machine translation engines to understand nuances, hues and conversational speech.