What Is AI Translation?

Computer-based translation has come a long way over the last 50 years, evolving from electronic dictionary conversions to translation engines powered by artificial intelligence. Recent breakthroughs have delivered tools that can go well beyond word-for-word translations. With the ability to comprehend syntax, grammar, and context, AI has made complex language translation as easy as using a website.

AI Language Translation Explained

The easy-to-use translations generative AI provides for a variety of audiences has changed many aspects of the translation industry, similar to how automation transformed manufacturing. One way AI is making an impact is via its ability to address a range of needs. For the casual user, AI text translation fulfills simple requests that don’t require much further review. Many use cases exist for such a lightweight service, including understanding a product page written in a foreign language or getting a general understanding of what’s happening in a video clip. For many of these instances, a person might previously have looked up words in a dictionary, asked for help, or perhaps not even tried at all. Now, AI language translation makes accessibility relatively effortless.

However, for areas such as legal documentation, emergency situations, or medical use, AI language translation currently works best as a support tool for professional translators and subject matter experts. It provides the best of both worlds: high-volume, easily accessed translations for simpler requests, along with tools to expedite more detailed work. This technology has allowed professionals to spend more time on deeper tasks that require absolute accuracy or strong cultural understanding, a particularly valuable capability as digital and remote work have reshaped the global economy and brought more languages into the fold.

How Is AI Used in Translation?

While translation algorithms are rapidly improving, the technology remains best suited for less mission-critical situations. AI works very well, for example, when the goal is a quick result without overly complex sentences or topics, involving languages with a large amount of training data.

Common ways that AI translation is used now include

• General translation: AI translation has given people a quick and easy way to understand foreign languages without having to rely on time-consuming translation methods, especially for situations where the user just wants a quick general understanding of content.
• Professional translators: AI has changed the way professional translators work. For critical documents, human expertise is still key for absolute accuracy. However, professionals now may work in conjunction with AI, where projects are given an AI translation as a first cut and the professional provides an edit. This is akin to how IT teams have used AI automation to take on smaller tasks, letting experts focus on more meaningful projects. Circumstances such as legal contracts, safety and repair instructions, and medical reports are ideal for using AI in conjunction with human subject matter experts.
• Real-time translation: In situations where a broadcast or stream uses subtitles and captions, AI translation can provide a real-time option to broaden accessibility with a variety of languages. Making this work may be as simple as a streamer adding a translation plug-in to its broadcast platform.
• Websites: AI translation lets websites offer content in multiple languages with reasonable accuracy. This can easily expand the audience of the website while offering an improved user experience, even for languages where the AI has limited training data.

How Does AI Translation Work?

AI translation works by taking in text in one language, processing it through a translation algorithm, and outputting text in a different language. Within that basic framework are a range of variations that affect speed, accuracy, depth, and complexity. Current AI translation systems can mass-translate large volumes of simple text—and when built on a cloud infrastructure with powerful semantic searches and vector capabilities, translation processes can execute quickly with fast output. However, because language is complex and dynamic, even the most powerful AI translation models are continuously evolving and improving to work in more situations.

The following are key points in the AI translation process:

• Machine learning in AI translation: Machine learning algorithms train translation models, often by using the same text in different languages to understand patterns of grammar, syntax, and context. Because machine learning is constantly learning from its processes, the model can begin to integrate greater complexity over time to improve both accuracy and efficiency.
• Neural networks’ role: For the most popular type of AI translation, neural networks play a key role as the engine underneath the neural machine translation process. Because neural networks can perform nonlinear analysis, such as pattern recognition and relationship assessments, they’re a critical part of addressing complex translation elements, such as syntax and context.
• Natural language processing: Complex AI translation models use a process that involves tokenizing words and phrases, encoding those tokens as vectors, then decoding those vectors into a different language. Natural language processing, or NLP, provides a mechanism for the tokenization process. With NLP, models can understand text and speech in forms of grammatical constructs (nouns or verbs, for example), syllables, phrases, names, and sentiment analysis, allowing for greater accuracy when applying vector encoding.
• Data training and language pairs: Language pairs provide the start and end points for AI translation models as an input language and an output language. Data training for these pairs usually involves taking the same text in different translations and building correlations between words, phrases, and syntax. These parallel texts may come from curated training text, professional translations, or known translations of published works.

Types of AI Translation

Several AI translation techniques are in use, with various levels of complexity and capability. The accuracy and scope of translations often correlate with the type of LLM; in addition, the speed and scalability of the model depends on the processing power and resources required. Therefore, organizations should consider their goals and resources when deciding what type of AI translation to use. For example, generative AI models provide a greater conceptual understanding of context and nuances, but they also require more processing resources.

The following are the most common types of AI translation:

• Rule-based AI translation: The most basic type of AI translation involves a combination of direct dictionary translations and the application of grammar rules. Because this process follows binary rules rather than advanced processes, the decisions involved tend to create a translation that’s technically accurate but with potentially awkward syntax. For short phrases and simple sentences, rule-based translation works well. However, for anything more complex, a sensible edit may be a valuable part of the process after the fact.
• Statistical AI translation: More advanced than rule-based translations, statistical AI translation uses machine learning trained on large amounts of language data. These models often focus on translating phrases instead of on a word-by-word level. The phrases are filled in by statistical likelihood based on the referenced material found in training data. These models lack the application of context or cultural nuance, which may lead to accuracy issues requiring further review.
• Neural machine translation (NMT): NMT is the current standard for AI translation. With NMT, neural networks are used to process language data in a way similar to how the human brain operates. NMT works by first taking in a phrase, then encoding the words as vectors in one language before decoding the vectors in another language. This process allows the neural network to examine the relationships between words as it weighs complexities in syntax and grammar. To optimize these processes, NMT models run best using clouds with native vector database capabilities for faster querying and results.
• Hybrid models: In some cases, a project can combine multiple methods to maximize output and accuracy. A common combination uses rule-based translation as a first cut for words, then processes that output through a more advanced method, such as statistical AI translation, for refinement. However, using a hybrid model comes with its own risks, including the fact that phrases are translated twice, thus inviting two steps that might elevate inaccuracies requiring further review.

Benefits of AI Translation for Enterprise

AI translation can benefit enterprises in many ways, both internally and externally. By taking advantage of AI translation, enterprises can adapt more quickly to an increasingly global workforce and customer base while also simplifying communication and operational needs. The following are some of the most common benefits of AI translation for enterprise:

• Cost savings: AI translation broadens an organization’s language capabilities without having to expand the number of multilingual professionals on staff. Now, that translation team can focus on complex or at-risk translations while AI handles the simpler, lower stakes cases to save time and money.
• Improved accuracy over time: AI translation models will naturally improve accuracy with the more translations processed by the algorithm. Every query is an opportunity for the AI model to learn and improve, and when applied on an enterprise scale, the output can be refined for better consistency and improved accuracy.
• Scalability across languages: With an AI translation tool embedded into operations, the platform can scale to accommodate additional languages based on need and solution availability. Whether natively incorporated or added as a plug-in, additional languages support better communication as both the workforce and customers become more diverse.
• Speed and efficiency: With a diversifying global market for both staff and customers, translation can affect operations, customer service, and other parts of an enterprise. Instead of relying on multilingual workers, AI translation can close language gaps well enough to keep things quickly moving forward while saving multilingual resources for more complex situations

Use Cases of AI Translation in Business

Not that long ago, translation was done by only a select group of organizations. However, the rise of remote work, a diversifying customer base, and a global economy have all led to an increased need for fast and cost-effective translation tools. AI translation easily fulfills this call with integrated language options in customer service platforms; fast, casual translations via a web-based tool; and other methods. The following are a range of common AI translation use cases in business.

• Energy and utilities: Energy and utility companies experience similar benefits from AI translation as other customer-facing organizations—better service across a diverse community, internal operations, and multilingual vendors. These types of companies can also use AI translation to efficiently deal with regional environmental communications, evolving regulations, and messages regarding emissions or grid issues.
• Financial services: Financial service providers have diverse customer bases and, often, global interests and partnerships. AI translation helps all of this, including interoffice communication, enhanced customer service, and report generation. AI can be particularly efficient with standardized materials, such as compliance filings, customer statements, and documentation.
• Government and public sector: Government agencies such as the DMV or IRS must work with a broad spectrum of languages and backgrounds. For communication and customer service, AI translation provides an easier path for support, such as multilingual chatbots or translation of emails in foreign languages.
• Healthcare and life sciences: While AI translation isn’t yet capable of supporting high-stakes medical communications, it can improve efficiencies in areas such as customer service, product descriptions, and clinical documentation. In this realm, AI often works best as a first cut translation tool with human oversight and editing, given the sensitive nature of the material.
• Manufacturing and the supply chain: With global sourcing and delivery, AI translation can rapidly improve manufacturing and supply chain process efficiency. Status updates, notices of problems or delays, logistics communications, and quality control can cross borders and languages, with AI translation offering a fast path to keep everyone on the same page.
• Telecommunications: Telecom companies service a broad range of customers and may have international networks. AI translation supports both internal and external communications on a business and technical level, along with providing translations for documentation and customer support content.

AI Translation Versus Human Translation

While AI translation is a rapidly evolving technology, gaps still exist between AI translation and human translation in some areas. To bridge these divides, the translation industry has shifted in a way that capitalizes on the benefits of AI translation while still relying on humans for specific use cases.

Key Differences

• Accuracy: As language is dependent on a combination of word choice, syntax, and grammar, so is the accuracy of a translation. A large language model’s knowledge base for weighing and synthesizing these elements may be vast for a language like English, but lesser-used languages will lack in available training data. Similarly, high-precision topics, such as medical or legal translations, may require additional reviews to ensure accuracy.
• Cultural context: In addition to accuracy, cultural sensitivities need to be applied with translation. While some types of cultural sensitivities can be easily addressed with broad rules, others require a nuanced understanding of regional and cultural preferences and customs. In particular, languages with limited training data will benefit from additional subject matter expert reviews for this issue.
• Lengthy content: On both a sentence and a document level, AI can struggle with lengthier content. On a sentence level, specifics can get lost in longer sentences due to structure, wording, and grammar. On a document level, AI translation may not always provide consistent structure and flow, or it may lose callbacks and other elements woven into the original text. While LLM developers are improving capabilities for these situations, organizations that need to translate longer documents may need more human intervention.
• Speeds: AI translation can process text much faster than a human. For simple translations without medical, legal, or safety consequences, AI translation can be the ideal choice.
• Uncommon languages: Languages used by smaller populations offer far less training data to work with versus a language like English or Mandarin. This creates significant accuracy problems for those languages, meaning humans fluent in rarely spoken languages have an edge.
• Writing style: A document’s writing style could be personal, such as a journalist’s intentional syntax, or based on industry, such as a scientific paper or medical journal. AI translation may accurately construe the meaning of sentences, but LLMs may struggle to replicate the intentionality of style. Similarly, proper nouns using standard language, such as product names and titles, can confuse a translation tool, as can specialized formatting in a document or web page. In these cases, a human translator can safeguard against errors with an additional review.

Perform Text Analysis and Machine Translation at Scale

For organizations that need to perform text analysis and machine translation at scale, Oracle Cloud Infrastructure (OCI) Language provides a powerful and efficient tool that can be integrated into applications. With sentiment analysis for more than 100 languages and translation for over 30 languages, OCI Language provides a secure, application-ready model capable of further customization.

AI language translation breaks down communication barriers, helping individuals and organizations disseminate information while fostering international collaboration, trade, and cultural exchange. Although the technology may not yet match the nuance of human translators, AI’s speed and scalability allow for rapid translation of large volumes of text and real-time communication.

AI Translation FAQs

What is AI translation and how does it differ from traditional translation methods?

AI translation refers to the computer-driven process of using an AI model to translate text. Traditionally, a bilingual human will review the text and methodically translate it into another language. This process, while sometimes slow and laborious, is normally highly accurate, given subject matter expertise for variables such as cultural context. AI translation offers automated, high-volume translations for a more efficient process, though current technology still requires a human translator for highly technical or sensitive text. Thus, AI translation is a great choice for simple, short, and low-risk use cases.

What industries can benefit most from AI translation?

Nearly any industry with multilingual customers, partners, and workers can benefit from AI translation, which offers a fast and accurate way to expedite communication between parties. Industries with complex and sensitive translation needs will want to continue to leverage human professionals, who can work in conjunction with an AI translator.

How accurate is AI translation compared with human translation?

It’s hard to pinpoint accuracy levels when comparing AI with human translators because translation tasks have several dynamic variables: length and complexity of the content, popularity of the language, cultural nuances, sensitivity of the topic, and intentionality of voice. In addition, translation training is dependent on several factors, including the quantity of training data available for a language and the type of translation technique used. A simple way to compare AI translation and human translation is to remember that AI translation is rapidly evolving and, depending on the topic and language involved, may create a highly reliable output. However, for topics where precision and sensitivity are key, a human translator can be relied upon for a final review.