Top Trends in Machine Translation

The localization industry strictly works on a “need-to-translate” basis, where a project is given to the translators and so does the process begins. In the past, due to limited resources translation service providers would work on one project at a time, keeping their entire focus to produce a single error-free outcome. 

However, rapid globalization has a much-changed translation industry with the advent of machine translation. Translation jobs are ongoing and require immense effort to translate  documents on demand. Machine translation is a way out to save time and is an effective trick in translations’ rule book.

Machine Translation is Always Evolving – Do You Think You are Ready for the Change?

Translation boosts the content, makes international reach to a wider audience possible in less amount of time. Therefore, the use of machine translation is more extensive than ever and it is fruitful for global businesses.

Now that new form of technology keeps springing up, there are a few considerable trends of machine translation in use by language service providers.

Neural Machine Translation

Neural machine translation consists of two major factors; cost and computing time. Proven to be elusive, neural machine translation is still quite a new application but a comprehensible one for automated translation.

This results in significant quality and productivity too; hence the use of new technology for translation is welcomed.  It is believed that the use of neural machine translation will improve the existing methods to translate content.

Does it represent any challenge?

Despite its popularity, one of the biggest challenges of neural machine translation is commercial exploitation. The reason is clear; the running cost involved in maintaining the powerful GPU (Graphics Processing Unit) is high. It does tend to take a toll on the language service providing company.

Content-Aware Machine Translation

Content is a vital basic root of the translation. A sentence by sentence translation is what machine translation is good for, right? But this does not mean that it picks out words and phrases on its own. Manual input is still required.

Certain words are hard to translate into the target language, which shows the limitation of the automated translation. For instance, the right translation for the Spanish word “sus” is hard to depict. Now if the software doesn’t have access to the previously translated document, chances are translation will go awry as there is no record of the word.

What challenges does it represent?

All automated translations are content-based. In the case of non-findings, the translated content will have errors. As the projects are completed, information is recorded and, therefore, it’s  ready to be used for the next project.

With time, automated translation is more likely to evolve and to emerge mostly for commercial use.

Translation Memories and Machine Translation Convergence

Do you know what is the most common mistake made by the organizations? They do not translate all content when required. To delay is to forfeit any efficiency in work.

Translation memories are the databases containing the sentence, paragraphs, and details used previously. It aids human translators, reduces time efficiently, and is an effective methodology too. For a while now, translation memory has become a convenient tool for human translators. Not only it reduces costs, but it also promotes the integrated workflow effectively.

With time, the more machine translation evolves; the use of translation memory will increase. Similar to neural machine translation, the projects will rely on the collective databases for a high-speed machine translation.

Multilingual Neural Machine Translation

Undoubtedly, neural machine translation is the most trending advancement of machine translation. Another cool feature of this version of machine translation is its ability to handle more than one language pair in a single domain system. A multilingual resource saves maximum costs for the language service provider and also provides fast translation with enriching linguistic resources.

Some of the other qualities of a multilingual neural machine translation make it beneficial  in numerous ways:

• Improves translation quality and shows great sustenance for the quality-oriented multilingual system.
• Fewer models are required to handle translation.
• Fewer costs are incurred for the training required to handle the databases, etc.

The use of a multilingual domain is most profitable for global businesses especially involved in advertising and marketing. This process automatically subsides any errors and quickens the process for localization itself.

Dynamic Adaption of Machine Translation

As the client base grows, the requirement for change in context is required on short notice too. This adds the stress on the modern MT systems to provide accurate translation for the client base.

Language service providers dealing with machine translation handles various projects at a single time. This means more than one language pair; domains, content, etc., are being engaged with that single database in use.

However, translation cannot rely on a single technology so a hybrid system is most effective as a dynamic approach towards better content. Translation memories are useful, while adding new instructions for the machine translation, the glossaries require updates too. For this purpose, the instance weighting forces the machine translation to target the right content and makes the most relevant use of the translation memories.

Likewise, machine translation is adaptable for instant web publishing like blogs, forums, community content, and so on.

Conclusion

In the past year, the monumental shift has given rise to many subsided efforts to increase the efficiency in machine translation. Some nations are more prone to use new technology and have a tendency to argue over the best way to utilize machine translation.

Most multilingual cultures like Asian countries are the reason behind the high demands of machine translation. The growth and wealth of the global business sector are directly descendent to such technological efforts. But what will it cost to the businesses, in the long run, is yet to be seen.