The Evolution of AI in Translation and its Implications for the Future

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In a matter of several years, generative and predictive AI has made great strides, bringing us closer to the future where people can communicate freely in different languages. How far is that goal and what are the implications of reaching it? Read on to find out.

Artificial intelligence (AI) already has a lot of traction in many fields, including writing books and assisting in medical research. But what about AI and translation? That’s the subject of this article. Let’s talk about how AI translation works, what are its strengths and weaknesses, and how it will impact the future of communication.

AI in translation: definition

The term AI refers to the methods and tools used by machines to imitate human intelligence. This technology is contingent on massive troves of data for the training of the algorithms. The result is the reproduction of behaviors that seem similar to human reasoning or even creativity. AI is currently used in many fields of activity, including transcription, customer service, and scientific research. It is also used for translation – the process of converting written or spoken words in one language into another.

How AI Translation Works

AI translation technology

AI translation technology works by breaking content down into smaller segments and applying algorithms to each segment. The algorithms analyze the content and generate a translation. The most common algorithms of AI translation are SMT and RBMT.

algorithms of AI translation

Rule-Based Machine Translation stems from the idea that language can be described with syntactic and grammatical rules. In their work, RBMT-based algorithms refer to bilingual dictionaries and rule books. Such a translation process is effective for language pairs that have very different structures. One example of such would be English and Chinese. However, it is also time-consuming.

Statistical Machine Translation is based on the concept of probabilities. The tool refers to a set of target segments and chooses the most probable ones as a match to the source segment. Simply put, SMT-based translation chooses the most statistically probable outputs. SMT is better than RMBT because it accommodates a wider range of languages and does not require an extensive list of resources for this.

What are the methods of translation with AI?

There are several methods of AI translation. Below, we’ll talk about the principal three.

Machine translation

Wikipedia traces the origins of machine translation to as far back as the 9th century CE and the works of Al-Kindi, an Arabic cryptographer. The idea was once again floated in the 17th century by René Descartes. But in earnest the work on machine translation began in the 1950s.

Machine translation refers to the process of automatically translating source language content into target language content. This is done without any human action. The problem with machine translation is that it considers individual words. That makes the translation literal. It has no focus on the greater context, hidden meanings, or relationships between content elements.

An example of machine translation would be the earlier versions of Google Translate.

Neural machine translation

The 2010s saw the birth of neural machine translation. This approach uses an artificial neural network, which predicts the likelihood of a sequence of words. NMT has several advantages over MT. The artificial neural networks require much fewer resources than statistical models used in MT. Neural machine translation algorithms also consider the entire text and its context. This results in translations that sound more fluid and human-like. NMT algorithms can also continuously refine their output, based on the data that they are fed.

An example of neural machine translation would be DeepL, which made a real splash in 2017 when it came out.

Translation software that uses AI

Translation software that uses AI

Today, regular users have access to software applications that use deep learning and machine learning to offer AI-powered translation. These include Systran, Microsoft Translator, ChatGPT, and others. Vidby also offers AI-powered translation through its tools, in particular, MeetUp. MeetUp is a software that enables real-time conversations in 70+ languages. It can also be used to offer near-simultaneous translation of conferences and symposiums.

Ethical Considerations for AI Translation

The use of AI in general raises a lot of ethical concerns, and AI translation is no exception. In this field, the ethical concerns are related to accuracy, bias, and cultural sensitivity.

Accuracy is critical for translations that need to be faithful to the original text. The difference between an accurate and inaccurate translation can mean a lot of things. These include pain and suffering, terminated contracts, and failed negotiations. It’s true that AI-powered translation can ease interpersonal communication across different languages. But relying solely on AI for accurate translations may be a grave mistake.

The accuracy of AI-powered translation tools depends on several factors. Quality and quantity of training data is essential. Complexity of the languages and the algorithms used also play a role. We need clear guidelines and protocols for the use of AI translation tools to address the accuracy concerns. This is especially true for sensitive fields such as legal or medical translation.

There are three types of bias in AI translation:

Data bias arises when the training data used to develop AI models is incomplete or contains eros.

Algorithmic bias happens when the AI model replicates and amplifies existing societal biases. Racial or gender biases are the first that come to mind.

User bias occurs when the users of AI platforms select translations that align with their own biases and perpetuate this in the feedback cycle.

To address the bias concerns, the training data must be representative, diverse, and free of errors. Developers should also be aware of potential biases and take steps to mitigate them.

Our culture shapes the language we speak, and the language reflects the culture. Accurate translation requires an understanding of the cultural context. This is true of both the source and target languages. AI translation systems may not always capture the nuances of languages they work with. This can lead to mistranslation and misunderstanding. The easiest example would be an AI system trained in American English that would struggle to accurately translate the text written in British English. The same can be said for many languages that have regional/country dialects. Certain words or phrases may have different meanings depending on the country of language’s use. Choosing the inappropriate translation can lead to confusion or even offense.

To address these concerns, AI translation systems must be designed to recognize and accommodate regional dialects, accents, and variations in language use.

What are the advantages and limitations of AI in translation?

Like any technology, AI translation has its advantages and limitations. Let’s consider them both.

The benefits of AI in translation

The most obvious benefits are the improved efficiency/speed of work and reduction of costs. AI-powered translation services can translate large quantities of text in a matter of minutes. They do not tire out and can work around the clock. The additional advantage is the cost-cutting. You still have to pay for post-editing services to check AI translations and ensure accuracy, but the final cost of the project will be lower.

The drawbacks of AI in translation

The drawbacks of AI translation all stem from ethical concerns. One drawback is that accuracy of AI translations is never 100%. For this reason, AI translations need a human professional review.

Another drawback is that generative models are trained for certain features. One is to produce smooth-sounding texts. The other – to select the most often used words. The former makes it harder to catch mistakes. The latter makes all AI produced texts sound similar after a while. For marketing communications, where text needs to make an impact, AI translation would be a waste of effort. That’s because AI-translated content will be bland, not creative.

AI is also unable to take into account the cultural context, the intent of the text, or the client’s wishes.

What is the impact of AI on the translation profession?

Given the limitations of AI translation, it is unlikely that AI will replace human translators altogether. This is especially true for fields where the slightest error may lead to damaging consequences. The examples would be medical, marketing, or legal translation.

We can, however, expect the AI to make the job of translators easier by providing first drafts of translations in a fraction of time it would take a human.

AI can also work well to facilitate multilingual dialogues about less complex topics, such as the conversations at hotel receptions, restaurants, and tourist offices.

What is the future of the translation profession in the face of AI?

The translation profession is unlikely to disappear because human translators have the expertise and cultural knowledge unmatched by the large language models. It will, however, undergo a certain transformation. The translators will have to learn to work in tandem with AI to accelerate their rate of output. They will also have to start offering clients services such as post-AI editing.

Which method of translation with AI performs best?

The neural machine translation is the most accurate and reliable method. It can consider the context of the content it’s translating, evolve over time, and improve autonomously.

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