What is Linguistic AI?
Linguistic artificial intelligence (AI) is a specialized branch of AI dedicated to enabling computers to understand, process, and generate human language, making interactions with technology more natural and intuitive. Central to this field are Large Language Models (LLMs), such as GPT, which are advanced AI systems trained on vast amounts of text. These models are crucial to many linguistic AI applications, as they replicate the nuances and complexities of human language.
Linguistic AI is transforming how we create, manage, translate, and interpret content across different languages, significantly enhancing the accessibility and efficiency of modern technology. Its applications are diverse, including automating tasks, responding to voice commands, generating content, and translating text between languages. By leveraging the capabilities of LLMs, linguistic AI is not only improving user experiences but also driving innovation in how we interact with and utilize technology globally.
More about Linguistic AI
How has Linguistic AI evolved?
One of the earliest forms of Linguistic AI is machine translation (MT), technology which automatically translates text or speech from one language to another. Since it was pioneered in the 1950’s, MT has undergone a significant transformation, delivering unprecedented quality throughout each evolution. The current form is Neural Machine Translation (NMT), which has been embedded into translation tools and leveraged by organizations across the world.
A pivotal development in NMT was the introduction of the transformer which allowed MT systems to capture complex word dependencies more effectively, leading to more fluent and accurate translations. As researchers expanded the size of transformer models, increasing the number of layers, parameters, and the amount of data used for training, models were able to understand and generate human-like text across a wide range of tasks, not just translation. Thus, the large language model (LLM) was born - examples include OpenAI’s GPT (Generative Pre-trained Transformer) and LaMDA (Language Model for Dialogue Applications), which can perform tasks like text completion, question answering, and creative writing with high accuracy. LLMs have now become the foundation for many cutting-edge AI applications, transforming industries and delivering far more than just translation capabilities.
To further enhance LLM output, organizations can opt to train or fine-tune general-purpose models in line with their industry and use case to make the models more effective and efficient. Larger companies with the necessary resources and expertise might choose to train or fine-tune LLMs in-house, so they have complete control over the process. Other companies without the internal infrastructure or expertise needed may choose to partner with an AI service provider that offers AI training and fine-tuning data services, such as TrainAI by RWS. These providers can collect and label domain-specific data, or the company’s proprietary data, to train the LLM and help fine-tune it using human-in-the-loop techniques (such as prompt engineering, reinforcement learning from human feedback (RLHF), and red teaming) to the company’s specific needs.
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 has become an integral part of numerous tools and applications, revolutionizing how industries operate across the board. From healthcare to finance, education to entertainment, and retail to customer service, virtually every sector is leveraging these advanced technologies to enhance communication, streamline processes, and deliver personalized experiences.
If we focus on the localization industry in particular, everyone across the supply chain can benefit from leveraging AI-powered translation technologies no matter their role – including language service providers (LSPs), translators and their clients:
- Boost productivity - With the right AI capabilities users can save valuable time and expedite time to market. Translators can complete work faster and more efficiently; project managers can streamline repetitive tasks; and reviewers can focus their attention more effectively.
- Expand translation capacity - By leveraging AI capabilities, users can handle larger workloads without compromising on speed or quality - an advantage particularly valuable for global businesses with extensive content demands. By using tools that integrate Linguistic AI capabilities throughout the entire localization process, it becomes easier to localize more content efficiently and accurately and connect with more customers in a positive, long-lasting way.
- Enhance translation quality - Linguistic AI not only translates text efficiently but can also be combined with your trusted linguistic resources to elevate translation quality from the start. It can ensure consistent terminology, refine both machine and human translations, and identify areas needing further review, for example. With real-time suggestions and context-aware adjustments, AI can support linguists by helping them improve their work and complete translations faster. With Linguistic AI technologies, companies can effortlessly deliver consistently high-quality content that aligns seamlessly with 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 quality.
- Natural language user interface: using natural language to search and access product documentation, generate reports or analyze projects.
- Automated speech to text: converting spoken language into written text using AI-driven transcription, enabling the translation of audio content within translation workflows.
- Automated post-editing: enhancing the quality of translated content through AI-driven post-editing.
- Automated quality scoring: evaluating and improving translations with automated quality assessment scores.
