1. Aurora AI™
Orange and purple aurora with the Lionbridge Aurora AI Array logo overlaying the image, representing the new customer interface.

Human Expertise Blended With Powerful AI

Lionbridge Aurora AI™ is an AI-first global content platform that increases your multilingual content creation and expands your audience with culturally relevant, hyper-personalized content.

mobile-toggle
  1. WHO WE ARE
Allie Fritz, Lionbridge’s Director of Interpretations

Meet the Pride: Allie Fritz

Lionbridge's Director of Interpretations

mobile-toggle

SELECT LANGUAGE:

Dynamic digital background

The Relevance of Machine Translation Amid a Generative AI World

Legacy translation engines are essential to AI-powered translation solutions, forming the foundation for fast, effective multilingual communication.

Tempted To Retire Machine Translation? Not So Fast.


Machine Translation tools continue to benefit enterprises that effectively incorporate them into their AI-driven translation workflow, even as Generative AI (GenAI) thrives.

As organizations adapt to a rapidly evolving translation landscape, they are finding that Machine Translation (MT) remains an indispensable foundation for scalable global communication.

While Generative AI (GenAI) and Large Language Models (LLMs) offer new possibilities for contextual understanding, top Neural Machine Translation (NMT) engines continue to deliver unrivaled speed.

By combining machine-generated translations, using NMT or Retrieval-Augmented Generation (RAG), with agentic AI post-editing prompt chains and targeted human oversight, enterprises can dramatically accelerate content delivery and reduce costs to achieve new levels of speed and scalability.

Here’s what the workflow looks like:
—Use MT for initial translations at the beginning of the process.
­—Use Generative AI solutions / LLMs for post-editing and quality assurance tasks to enhance overall quality.

This approach enables your organization to meet your translation demands with speed, quality, and cost efficiency.

The Benefits of Machine Translation as Its Role Evolves

Incorporating MT into your AI toolset and using it as the initial translation pass enhances efficiency, resulting in the following benefits.

Cost Savings

MT reduces human translation hours, cutting costs and freeing up humans for important oversight work.

Faster Turnaround Times

MT processes translations at speeds unmatched by human and LLM capabilities.

Greater Scale

MT handles large volumes of content effortlessly.

How AI-Powered MT Supports Major Verticals

Machine Translation can help overcome business challenges across industries. See how our MT and AI-powered solutions support legal, life sciences, and e-commerce customers.

Legal

Global legal cases may generate high volumes of multilingual legal eDiscovery documents that must be translated quickly. They may also require more official, high-level documents for court submission, etc. Learn when Machine Translation can translate legal documents accurately, efficiently, and cost-effectively, and four reasons AI translation should enhance, not replace, professional legal translation services.

Life Sciences

While AI must be used in ways that control risk and protect end users and compliance, it's also a key tool for providing life sciences language services at scale. AI-supported, expert-led language services help companies involved in drug and medical device development meet tight deadlines, manage increasing volumes of documentation, and enter new markets. Discover how MT and AI are helping life sciences companies create better patient outcomes.

E-commerce

E-commerce is a critical way for companies to reach global markets. AI-enhanced MT can help these companies create a faster, steadier stream of engaging, personalized content for customers — regardless of their language. Learn how retailers have already been using MT and AI to connect with customers worldwide.

Comparing Four Machine Translation Deployment Strategies


Machine Translation solutions comprise the following four basic strategies.

Public MT

This strategy includes services such as Google Translate and Bing Translator. Such services are readily available at no cost. However, the strategy’s shortcomings include security and quality issues in certain situations, as engines have not been trained for specific domains or particular use cases.

On-Premises MT

This strategy requires a company to deploy an MT server in its IT environment. While this is the most secure option, it comes at a high cost, is complex to deploy and manage, and requires ongoing maintenance. Importantly, this strategy often produces suboptimal MT output across multiple language pairs or content types.

Cloud MT

This strategy works like the public MT option because it is also hosted in the cloud. However, unlike public MT, it creates a company-dedicated instance. Any data provided to the service is tightly secured and not shared with third parties. It delivers additional capabilities around terminology customization and has other benefits. However, it can result in vendor lock-in and less-than-optimal Machine Translation quality across multiple language pairs.

Best-of-Breed MT

This strategy involves a single platform that allows companies to leverage multiple engines. It provides a single layer of terminology customization, an easy-to-manage interface, and the ability to choose the best option for different language pairs, industries/domains, and content types. This approach, offered by Lionbridge, is designed to deliver the best translation results and is a key differentiator for the company.

Dig Deeper Into Content About Automated Translation

Webinar Recap: Can AI Post-Edit?

Learn about Cisco’s real-world experience using NMT for initial translations, LLM-powered AI post-editing to refine the output, and human testers for functional in-context review to translate 15 million words into 14 languages in just three months.

Lionbridge Aurora AI

The Lionbridge Aurora AI platform orchestrates the entire global content lifecycle, from ingesting customer data to translating it with Machine Translation (MT), using LLMs for automated post-editing, and then delivering it back into content repositories.

Smart MT™: Enterprise-Grade Machine Translation + AI

Discover Lionbridge’s enterprise-grade Machine Translation and AI solutions that utilize the best MT engines and AI reviews to enhance global communications.

Machine Translation Customization vs. Machine Translation Training

Machine Translation customization vs. Machine Translation training: Learn what they are and when to use each method to improve automated translations.

Language Ranking Based on Machine Translatability for More Effective MT

Consider language complexity before deploying Machine Translation. Our machine-translatability ranking will help support your business decisions.

Machine Translation in Translation

Stay up to date on the key terms in Machine Translation with this handy cheat sheet.

Meet Our Machine Translation and Generative AI Experts

With deep AI expertise, you can feel confident about Lionbridge’s Machine Translation services and generative AI language services.

Vincent Henderson

Chief Product Officer

Vincent leads Lionbridge’s product and development teams. He focuses on ways to use technology and AI to analyze, evaluate, process, and generate global content. He is especially attentive to the disruption caused by Large Language Models (LLMs) to content products and services.

Share on LinkedIn

Rafa Moral

Vice President, Innovation 

Rafa oversees R&D activities related to language and translation, including custom LLMs for Machine Translation, Q&A assistants, and other tasks using RAG, fine-tuning, and other LLM customization techniques.

Share on LinkedIn

A Brief History of Machine Translation

1954 - Georgetown researchers perform the first-ever public demonstration of an early MT system.

1962 - The Association for Machine Translation and Computational Linguistics is formed in the U.S.

1964 – The National Academy of Sciences forms a committee (ALPAC) to study MT.

1970 - The French Textile Institute begins to translate abstracts using an MT system.

1978 - Systran begins to translate technical manuals.

1989 - Trados is the first to develop and market translation memory technology.

1991 - The first commercial MT system between Russian, English, and German-Ukrainian is developed at Kharkov State University.

1996 - Systran and Babelfish offer free translation of small texts on the web.

2002 - Lionbridge executes its first commercial MT project using its rule-based MT engine.

Mid-2000s - Statistical MT systems launch to the public. Google Translate launches in 2006, and Microsoft Live Translator launches in 2007.

2012 - Google announces that Google Translate translates enough text to fill 1 million books every day.

2016 – Both Google and Microsoft enable Neural Machine Translation (NMT), slashing word-order mistakes and significantly improving lexicon and grammar.

2020 - As of October, Google Neural Machine Translation (GNMT) supports 109 languages.

2022 - Lionbridge experts share findings that MT engine output performance is stagnating, and all tracked engines perform similarly. This development indicates that the Neural MT paradigm may be reaching a plateau and suggests a new paradigm shift could be approaching.

2022 - OpenAI launches its generative AI engine, ChatGPT, to the public in November, highlighting an evolving and expanding translation technology landscape.

2023 - GenAI proliferates with more model launches, a steady stream of new iterations, and solutions catering to various industries and use cases.

2024-present – MT’s relevance shifts, complementing LLMs as generative AI flourishes.

Get In Touch

Business Email Only