AI and Language Translation Services
Introduction
For organizations around the world that have products or services with a STEM origin (Science, Technology, Engineering, Math), there is nothing as important as accurate language translations for their world-wide constituents. Whether it be for patients involved in multi-country clinical trials, technically dense documents requiring translators with the relevant technical experience, or even Intellectual Property (IP) based legal matters, the need for accurate translation with fast turnaround times and cost-efficiency has become essential in this increasingly globalized world. In this blog we’ll take a look at how AI translation services are changing the industry.
Importance Of Language Translation Services
Language translation services have always played an important part in providing accurate translations to such organizations. A language translation service provides the ability to convert written or spoken content from one language into another. This service is vital for bridging communication gaps in various sectors, such as business, healthcare, or legal, where highly technical translations are commonly required. Professional translators ensure that the essence, tone, and context of the original message are preserved in the translated version, which enables effective and accurate communication across different cultures and languages.
How AI Has Transformed These Services
Artificial intelligence has completely transformed language translation services, making them more accurate, faster, and cost effective to complete. Machine learning algorithms (ML) can now better understand context and cultural nuances, which results in translations that are closer to what a trained linguist would be expected to produce. AI-driven tools such as Google Translate are now common-place, not just at large organizations, but also being used by individual consumers for their own needs.
ML/AI technology continues to evolve, promising even more sophisticated language translation capabilities in the future.
Read out recent Blog Top Three Pros And Cons of using AI In Medical Translations.
History Of Language Translation Services
The 20th century has seen significant innovations in language translation services. Translation was originally a manual process, with successful outcomes requiring skilled linguists to translate texts by hand. In the 1950s, the first ML-based translation programs emerged, notably from IBM. By the latter half of the century, advancements in computational linguistics and the advent of the internet transformed translation services, leading to the creation of more sophisticated translation tools.
Read out recent Blog Breakthrough Technologies In Medical Translation.
Below is a summary of the difference between traditional translation methods vs. the present-day ML-enhanced approach:
Traditional Language Translation Methods:
- Manual Translation: Skilled human translators, often bilingual or multilingual, manually convert text from one language to another, ensuring cultural nuances and idiomatic expressions are accurately represented.
- Linguistic Expertise: Traditional methods rely heavily on the translator’s deep understanding of both the source and target languages, including grammar, syntax, and cultural context.
- Accuracy and Style: Human translators can provide high levels of accuracy and adapt the style of the text to suit the intended audience, ensuring a natural and fluid translation.
ML-Enhanced Translations:
- Speed and Efficiency: ML-enhanced translations, powered by neural machine translation (NMT) models, can process vast amounts of text quickly, making them ideal for real-time or large-scale translation needs.
- Constant Learning: Modern ML (a.k.a. AI) systems continually improve through machine learning, allowing them to adapt to new linguistic patterns and improve accuracy over time.
- Accessibility: ML-driven translation tools, such as those provided by platforms like Google Translate, are readily accessible to the public, often available for free or at low cost.
How AI Translates Languages
AI translates languages by leveraging ML and natural language processing (NLP). These systems are trained on vast amounts of multilingual text, learning patterns, and linguistic rules. When you input a sentence for translation, the AI processes the text, understanding context and grammar. It then maps this to the target language, ensuring the translated output maintains the original meaning, tone, and style. Over time, as more data is fed into these models, their accuracy and fluency improve, enabling more natural and contextually relevant translations.
How NLP works:
NLP works by enabling computers to understand, interpret, and generate human language. It breaks down text into smaller units, which identifies the grammatical parts of words. ML algorithms are then used to analyze these elements, extracting patterns and meanings from vast datasets. NLP also aims to understand the context and intent behind words and phrases. NLP bridges the gap between human communication and computer comprehension, allowing for better natural interactions with technology.
AI-Enabled Translation vs. AI Human-Assisted Translation:
According to an excerpt from Speakt, AI-enabled Translation refers to the use of Natural Language Processing (NLP) and other AI learning algorithms to translate text from one language to another. The perceived goal of AI-enabled Translation is to provide accurate translations quickly and efficiently.
There are two main approaches for how AI-enabled Translation is utilized:
- Rule-based translation, which relies on pre-defined rules and dictionaries to translate.
- Statistical-models translation, which uses statistical models built upon large amounts of data in order to translate with a higher degree of quality.
AI Human-Assisted Translation is the human verification and authentication of translations initiated via AI. It combines the strengths of both to overcome the limitations of pure automation. This methodology involves accredited human translators working alongside AI systems to enhance translation accuracy, contextual quality and localization.
In the human step of the process, an expert language reviewer checks and edits the AI-enabled Translation before it is considered final. Human experts also help in the training of AI programs over time to generate more accurate context and translations that are personalized to each project.
Read out recent Blog Comparison Between AI-enabled Translation And AI Human-Assisted Translation In Clinical Trials .
Leading AI Translation Services Tools
There are many such tools now in the marketplace. While not exhaustive, the following are the best known with their key feature:
- Google Translate is one of the most popular tools, offering translations for over 100 languages and integrating seamlessly with other Google services.
- DeepL is known for its nuanced translations and user-friendly interface, making it a favorite for those who need high-quality translations.
- Amazon Translate provides robust API integration, ideal for businesses looking to scale their translation needs.
Each tool has its unique strengths, catering to the different situations facing organizations requiring language translation services.
Future AI Innovations For Language Translation Services
Future AI innovations for language translation services are set to further enhance accuracy in translations across linguistic barriers. The below list is some of the expected improvements:
- Real-time, context-aware translation that not only translates words but also understands idiomatic expressions, cultural nuances, and the speaker’s tone to provide accurate and natural-sounding translations.
- Personalized translation models that learn from the user’s language preferences and usage patterns to offer more tailored translations.
- AI-driven translation services that integrate seamlessly with Augmented Reality (AR), enabling users to instantly translate and understand written text in their physical environment through smart glasses or mobile devices.
- Advancements in speech recognition and synthesis that allow for more fluid and natural real-time voice translations during conversations.
- ML algorithms that continuously improve the accuracy and efficiency of translations by learning from vast amounts of multilingual data.
Conclusion
While AI has made tremendous advancements over the years, it still doesn’t possess the perfect emotional intelligence required to make the kind of accuracy decisions necessary for highly technical material to be translated. There are too many ambiguous terms, local nuances and underlying intentions that AI-enabled translations cannot be considered sufficient on their own. According to an excerpt from a Forbes Council article, “AI is great at interpreting billions of data points, but when it comes to human emotion, we still need humans to understand and explain why a decision was made. One might argue that this reality will actually make humans more valuable in the workplace.”
Brian Cantwell Smith’s famous quote on AI applies here. Companies should use AI-enabled translations to “shoulder the reckoning tasks at which they excel, and not for other tasks beyond their capacity.” Human experts with creativity, reasoning and industry experience can supplement these mechanical tasks with reviewing and editing power for all materials being translated, before they are considered final and placed in the public sphere.
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