Facebook’s translation feature produced some truly comical results when it was still in its infancy stages. However, over the last few years, it’s improved. This reformation can be credited to a combination of user suggestions and its clever use of artificial intelligence (AI) and machine learning. In 2020, they introduced a new open-source polyglot AI model.
It could translate between 100 languages without relying on English as a base language. It went as far as achieving first place at an annual multilingual speech translation competition. Social media marketing for foreign languages is crucial. But how can you leverage artificial intelligence and machine translation models like Facebook’s M2M-100 to produce faster language translations for your marketing and business goals? This question is what the following guide endeavors to answer.
What is machine translation?
When we talk about translation AIs, you must understand what options you have at your disposal. The field most concerned with using computers and software to translate one language to another is known as machine translation. It’s important to note that it has not always involved artificial intelligence. Research on this field first began in 1951 by Yehoshua Bar-Hillel at MIT.
Nevertheless, machine translation has become so intertwined with AI, machine learning, and deep learning that it’s now considered a field in those studies. Thus, it’s important that we examine how artificial intelligence has influenced and impacted machine translation.
Traditionally, there were two types of machine translation engines – rule-based (RBMT) and statistical (SMT). As you might expect, they vary in processing information and data. However, we often combine them into a singular engine known as a hybrid MT.
Rule-based machine translation operates on three key translation paradigms:
- Interlingual machine translation
- Transfer-based machine translation
- Dictionary-based translation
Essentially, it uses linguistic rules to analyze and process content. RBMTs are primarily used to construct online dictionaries, text processors, and grammar checkers (such as Grammarly). One of the greatest advantages of targeted rule-based engines is that they don’t require a large and structured text volume (AKA bilingual corpus).
Contrastingly, statistical machine translations (SMT) do. They generate translations by processing a bilingual corpus. As such, they don’t analyze text based on a set of language rules. This process requires a large group of bilingual content to perform translations effectively.
The evolution of machine translation and AI
As with any field of study concerned with computing, machine learning is evolving. Since 2013, we’ve seen a trend where large software and internet corporations such as Google and Amazon began to investigate the possibility of integrating neural networks into their translation models.
In artificial intelligence, neural networks are a collection of perceptrons working together to mimic the human brain. Perceptrons are computational representations of biological neurons (brain cells). Neural networks are commonly used for image, video, and speech recognition.
Neural network’s use in machine translation allows empty engines to self-train by applying a technique known as deep learning. We’ve already seen how it has changed the face of content marketing and how it can provide resources to help companies and individuals succeed in sales. But it’s still too early to predict its future impact and potential. Nevertheless, if we examine how Meta (formerly Facebook) has deployed its neural network-based translation AI, the results are encouraging.
It has been shown to generate translations that are smoother and more human-like. Several language pairs on Google Translate and Microsoft Translate have already switched to neural MT. Additionally, we’ve already seen an increase in adoption from many professional machine translation systems such as Systran.
Which machine translation engine is right for you?
As we’ve seen, each translation engine processes and produces information differently. However, in most cases, machine translation is used in combination with translation memory matches – regardless of the machine translation model you use.
Segments of content that existing translation memories have not leveraged are targeted and processed through one of the engines. These segments include new or heavily modified content. The raw translation output generated is then post-edited by experienced linguists as either accepted or modified before being inserted into the translated document. This combined method produces the best results in terms of time and quality.
Technical content such as user assistance content, customer support, and user documentation is considered the most suitable type of content for machine translation. The suitability of content increases if the content has been optimized for machine translation.
We propose that you implement two quality-at-source steps to increase the quality of your content and optimize it for machine translation. Machine translation works best with repetitive and straightforward content.
Thus, it would be best to utilize controlled English guidelines when you author new content. You can achieve this by creating clearly defined glossaries or terminology lists before initiating the translation process.
With this system, you can reduce translation costs and increase consistency across projects. But these aren’t the only benefits…
Benefits of using machine translation AI for your business
Implementing a machine translation model can allow you to achieve new efficiencies in your translation process. Moreover, it can provide you with a more cost-effective solution for your next translation project.
By applying MT, you will reduce time to market, allowing you to get paid faster as well. Moreover, you’ll also be able to increase productivity and improve terminology consistency.
Another way that machine translation can boost your total productivity is via translating larger content volumes. On average, machine translation AIs produce over 8000 translated words each day. This figure is in contrast to the 2500 translations with human translation. By translating more content at quicker rates, machine translation also helps reduce your time-to-market, allowing you to translate more words per day and deliver translated content to your customers quickly.
Other benefits of leveraging AI for translation
This guide spoke in length about how AI-powered machine translation can streamline your translation process and save you money. However, you need not replace human translators with AI ones.
Instead, you can build a more efficient system by leveraging AI along with human operators. For instance, machines can handle the bulk of the translation while human reviewers scan the translations for any errors due to localizations or colloquialisms. This entire process increases the speed and accuracy of your translation system.
Furthermore, AI can also help you organize and manage your translation staff. If your company works with a large variety of languages, some of which may be obscure, you must keep a record of which translator is working on which project.
Additionally, AI can help you keep track of their progress. It can also help you judge the performance of your translators. Even if your company specializes in translating a singular language, you may find that some translators are better at certain tasks or subjects than others. You can use an AI to produce this information and streamline your business accordingly.
We’ve seen the macro-ripples that artificial intelligence and machine learning have caused in business, from web design to SEO.
Its benefits in language translation should be no surprise, especially if you consider all the strides computer scientists have made in natural language processing.
While AI may not completely replace human translators and reviewers right now, we may see more jobs become redundant in this sector in the next ten years. Nevertheless, while the technology is not perfect, you can and should still leverage the power of AI to improve the efficiency of your business and its operations.
Guest author: Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.
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