While languages are dying rapidly, the speakers of endangered languages are using technology to transmit their distinctive languages and culture to the future generation.
The United Nations declared 2019 the International Year of Indigenous Languages to raise awareness of the dire situation of languages in danger of disappearing.
“Through the language, people can preserve their communities’ tradition, culture, customs, memories, and distinctive ways they think, have meaning and expression.
They also utilize it to create their future. Language is crucial in areas such as protection of human rights and good governance as well as reconciliation, peacebuilding, and sustainable development.”
AI Is Becoming Crucial
One of the issues artificial intelligence has proven is using artificial intelligence systems to convert one language spoken by humans to another. Also called MT, automatic translation, or computer-generated translation, machine translation’s purpose in AI is the ability to effortlessly, quickly, and precisely translate one written or spoken language into another.
Human Languages
Although the fundamentals of machine translation are simple to grasp, making the automated translation of languages is incredibly complicated. Human languages are characterized by various subtleties, slang, and related words.
Some languages even have words that can have different meanings. Computers must be able to comprehend and process these languages’ nuances to give a precise translation.
Incorporating Technologies
To achieve this, a range of technologies must be incorporated to make it happen, including cutting-edge technology like deep learning artificial intelligence, big data, and linguistics analysis.
Some of MT’s technologies have been in use for quite a long time, like cloud computing, cloud storage, and web APIs. These are being utilized together with new technologies to power translation.
Recent Evolution In Machine Translation
Today, three main kinds of machine translation are available which are:
- Rule-based translation (RBMT)
- Statistical machine translation,
- Hybrid systems that mix both.
The name suggests that rule-based machine translators use rules that determine how something is translated. In essence, it uses two dictionaries and then uses both of them to produce an actual translation.
In the last couple of years, most machine translation applications have used rule-based machine translation because this was one of the first methods to obtain accurate results. It is ideal for technical tasks since it offers literal translations and has a lengthy background.
However, dictionaries only offer a limited amount of translation because many words aren’t well translated between languages. To improve its translation, the method of solving was developed, dubbed Statistical Machine Translation. Instead of using only dictionaries for translation, these computers can learn about translations by analyzing the texts in two languages.
Statistical Machine Translation Systems
As technology advances, AI and RBMT approaches no anymore dominate the field. Most implementations have moved to statistical machine translation systems or hybrid ones as they offer translation based on actual documents rather than a pre-input dictionary. Hybrid machine translation brings both approaches. Dictionaries are the foundation for translation, while the computer can also comprehend multilingual texts to learn the specifics of human language.
Reliance on Information
Machine learned-based translation relies on massive quantities of information; it ought not to be a surprise that major cloud companies are at the forefront of this with solid machine translation technologies. Amazon, Google, Microsoft, Facebook, and others have created powerful machine translation tools that draw on the limitless conversations occurring on their platforms in many languages.
Unsupervised Learning Method
Google translate has enjoyed widespread use, albeit with some significant issues with its accuracy. Facebook announced an unsupervised learning method for machine translation which has proven to be more accurate. Amazon also launched machine translation for the Amazon Web Services (AWS) platform.
Alongside the major cloud providers, at most 45 machine translation firms operate around the globe. Many translation agencies, such as Professional Arabic Translation Services, specialize in translation services that aim to translate professional documents.
Others make human-in-the-loop techniques to enhance the machine translation process or deal with cases that have accuracy below acceptable levels. Services offering machine translation for business and professional reasons have been gaining popularity. These fully automated services permit companies to save lots of time and money when it comes to having content translated.
Machine translation with AI can handle more than three times the volume of work that humans are capable of handling. And this includes the human editor going through the creation of the machine. Hence, we are seeing agencies like professional Farsi translation services becoming more apparent.
Wrapping Up
Machine translation using AI has advanced significantly since the early days of translators. With AI today, human beings can consider many more details when translating.
Innovative technologies and new learning techniques can help improve machine translation as time passes. As we discussed earlier in our passage, translating is a complex problem, but machines are getting more adept in this area. They are finally becoming more able to understand the cultures of the world when translating content from one language to another.
