What is linguistic AI?

Linguistic artificial intelligence (AI) pertains to a subset of AI that focuses on enabling computers to understand, interpret, and generate human language. It draws from research and development in artificial intelligence such as neural networks, deep learning but specifically applied to natural language processing (NLP), natural language generation (NLG), linguistics, and related fields. 
Linguistic AI revolutionizes how content is created, managed, translated, and understood across languages worldwide. It encompasses AI systems and technologies designed to comprehend, process, generate, and interact with human language. Prominent examples of AI systems include large language models like GPT and BERT, and virtual assistants like Siri and Alexa. 
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More about linguistic AI

How does linguistic AI work?

Linguistic AI relies on content to understand and transform, and it can also be harnessed to generate new content. Let’s consider the basic building blocks of linguistic AI to better understand how it can be applied: 
  • Language understanding involves the application of deep learning algorithms to extract meaning from vast amounts of content. It goes beyond converting words into data structures, which computers require to interpret data. For example, semantic AI is AI that appears to understand what content means by analyzing content through NLP. This process aids individuals and organizations in various stages of the content management and translation management process.  
  • Language transformation involves a machine-first approach to translation. Machine translation serves as a straightforward example, it can globalize chatbots and enhance translator efficiency.  
  • Language creation involves harnessing LLMs to generate new content. LLMs are a type of artificial neural network that has been trained on large amounts of text data to generate natural language text or perform language-related tasks. This can provide writers, or translators, with a head start through content generation or generative translation. However, it's important to note that we're not implying that machines will write the next great novel or that creative writing and translation no longer require human input. 

What are the business benefits of linguistic AI?

In recent years, artificial intelligence (AI) has evolved from a novelty to a potential necessity. Linguistic AI is now applied across various domains, including customer service, healthcare, content creation, sentiment analysis and more. If we focus on the localization industry, it offers benefits to the entire sector – including language service providers (LSPs), translators and their clients: 
  • Expedited time-to-market. Appropriate use of the right linguistic AI features in translation technology can save users valuable time by accelerating their tasks and optimizing their efforts, such as speeding up translation tasks for linguists and streamlining repetitive project management duties. This means less time spent on manual, time-consuming actions that offer limited value.
  • Increased translation scale. Appropriate use of the right linguistic AI capabilities lets users produce translations in a fraction of the time, resulting in the creation of more translated content with the same resources. Thanks to the efficiency of linguistic AI and other translation technology, funds previously earmarked for tasks that can now be automated can be reallocated and spent elsewhere.  
  • Enhanced translation quality. Every market has its unique nuances when it comes to content. To ensure that localized content is suitable for each market, AI tools can help to improve translations done by human linguists or machines, ensuring the correct terminology and tone in their translations. This enables companies to effortlessly reach global audiences with consistent copy, maintaining their brand tone and voice. 

What are linguistic AI capabilities in translation technology?

The language industry has been significantly disrupted by artificial intelligence, with developments appearing thick and fast. Examples of linguistic AI capabilities in translation technology include:  
  • Neural machine translation (NMT) with adaptable language pairs: NMT systems trained on existing translation memory and termbase data, with automatic post-editing feedback. 
  • Content analysis: extracting domain classifications and keywords to help project managers focus on the big picture rather than process management.
  • Retrieval-augmented generation (RAG): supplementing LLMs with input from translation memory (TM), terminology databases and NMT for improved translation efficiency. 
  • Automated post-editing: enhancing the quality of translated content through AI-driven post-editing. 
  • Natural language user interface: using natural language to search and access product documentation, generate reports or analyze projects. 
  • Automated quality scoring: evaluating and improving translations with automated quality assessment scores.