Quality translations are always desired for business and communication. Professional translations are the backbone of global marketing and international branding. Now, businesses, professionals, and personal relations rely on the quality and transparency of bilateral communication. Up until recently, it was a hectic and costly task to cross the language barrier irrespective of the field or language pairs. Currently, technology has advanced at a super fast speed. Unprecedented innovations continue to ease the workload of humans.
Mainly, due to the advancements in natural language processing, deep learning, artificial intelligence, and progress in overall computing efficiency, the field of machine translation service has become one of the biggest beneficiaries of technological innovations.
Most scalability-friendly companies are actively using machine translations to execute their localization and marketing-related tasks. Additionally, machine translation is used to ensure and deliver the quality of translations, eliminating manual procedures and cutting costs and time. Let’s briefly explore how machine translation works, the key types of machine translations, their applications, benefits, and shortcomings.
How Exactly Does Machine Translation Work?
Machine translation is the process of providing linguistic input to the software. The machine translation software then splits the source text into different chunks and translates these chunks into the target language of your choice. Different machine translation software offer different workflows, benefits, language pairs, and scalability options. Most importantly, to ensure linguistic quality and contextual relevance, most businesses, brands, and companies prefer machine translation post editing services over just machine translation.
Take the example of Wuthering Waves, a famous RPG game, in its latest release some serious translation and localization flaws caused an uproar in the gaming community. To eliminate the chances of translation errors, it is strongly advised to acquire machine translation post editing services (MTPE) services. In the MTPE service, your source text is analyzed in the advanced machine translation tool and then subject-matter expert native translators and editors review the target file for any linguistic, technical, cultural, and contextual inaccuracy. The key prerequisites of machine translation involve a large database, and most importantly, machine translations can accommodate large quantities of text and produce accurate translations according to the industry, language pair, and marketing preferences of the business/brand.
Steps of Machine Translation
Details
Training
The machine translation system is trained with massive amounts of already translated text including both the source and target language versions.
Pattern Recognition
Depending on the MT type and data, linguistic rules are applied.
Pattern recognition and complex relationships between languages are identified.
Translation
With pattern recognition and neural network recognition, desired outcomes are generated in the target language.
Results
The translated text is produced in the target language.
Types of Machine Translations
Before machine translation became a buzzword, machine learning, and deep learning were used in the industry of neural networking. Take the example of the software structure of self-driving Tesla cars. This is another example of the use case of machine learning in everyday use other than language learning and language translations. The application of machine learning in the linguistic field is far more vast and complex than the self-driving software of Tesla. However, there are diverse fields and specific requirements for every niche, especially when it comes to linguistic translations involving source and target languages. Let's take a look at the key types of machine translations and their pros and cons.
Types of Machine Translations
Details
Applications
Statistical Machine Translation (SMT)
Examines large quantities of bilingual text (data) to identify statistical relevance for translation.
News articles, product descriptions, and social media content.
Neural Machine Translation (NMT)
Trained in artificial neural networks and can handle huge amounts of bilingual text data to learn complex relationships between language strings.
Marketing materials, legal documents, and creative content.
Rule-Based MT (RBMT)
For accuracy and linguistic quality, RMBT adheres to linguistic rules and dictionaries while translating text strings.
Technical documents, manuals, glossaries (with pre-defined terminology)
Hybrid Machine Translation (HMT)
Uses both RBMT and SMT or NMT approaches for more comprehensive and quality translations.
Complex technical documents and legal contracts (combines rule-based accuracy with NMT fluency)
Contact a Machine Translation Service Provider Today!
Gone are the days when human translators were the only solution for any type of business or personal translation needs. With technological advancements and growing demand for quality translations, language service providers (LSPs) use machine translations for quick and accurate results. Moreover, to ensure linguistic quality and adherence to the cultural nuances of the target market, leading LSPs are offering machine translation post-editing (MTPE) services. Machine translation is shaping the future in less time and with increasingly better results. If your company is also going global and wants to cut hefty translation costs, hire a machine translation service provider and reduce the cost of translations in less time.