This post is co-written with Keith Brazil, Julien Didier, and Bryan Rand from TransPerfect.
TransPerfect, a global leader in language and technology solutions, serves a diverse array of industries. Founded in 1992, TransPerfect has grown into an enterprise with over 10,000 employees in more than 140 cities on six continents. The company offers a broad spectrum of services, including translation, localization, interpretation, multicultural marketing, website globalization, subtitling, voiceovers, and legal support services. TransPerfect also uses cutting-edge technology to offer AI-driven language solutions, such as its proprietary translation management system, GlobalLink.
This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate Amazon Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.
Amazon Bedrock is a fully managed service that simplifies the deployment and management of generative AI models. It offers access to a variety of foundation models (FMs), enabling developers to build and scale AI applications efficiently. Amazon Bedrock is designed to be highly scalable, secure, and straightforward to integrate with other AWS services, making it suitable for a broad array of use cases, including language translation.
The AWS Customer Channel Technology – Localization Team is a long-standing TransPerfect customer. The team manages the end-to-end localization process of digital content at AWS, including webpages, technical documentation, ebooks, banners, videos, and more. The AWS team handles billions of words in multiple languages across digital assets. Given the growing demand for multilingual content by internationally minded businesses and new local cloud adoption journeys, the AWS team needs to support an ever-increasing load and a wider set of languages. To do so, the team relies on the GlobalLink technology suite to optimize and automate translation processes.
The challenge
The AWS team and TransPerfect created streamlined custom workflows and toolsets that enable the translation and delivery of billions of words each year. Content localization is a multi-step process consisting minimally of asset handoff, asset preprocessing, machine translation, post-editing, quality review cycles, and asset handback. These steps are often manual, costly, and time-consuming. AWS and TransPerfect are continually striving to optimize this workflow to enable the processing of more content at a lower cost and to decrease those assets’ time to market—providing valuable, salient content faster for non-English-speaking customers. Additionally, transcreation of creative content posed a unique challenge, because it traditionally required highly skilled human linguists and was resistant to automation, resulting in higher costs and longer turnaround times. To address these issues, TransPerfect worked with AWS to evaluate generative AI-powered initiatives for transcreation and automatic post-editing within TransPerfect’s GlobalLink architecture.
Security and data safety
Amazon Bedrock helps make sure data is neither shared with FM providers nor used to improve base models. Amazon Bedrock adheres to major compliance standards like ISO and SOC and is also a FedRAMP-authorized service, making it suitable for government contracts. The extensive monitoring and logging capabilities of Amazon Bedrock allow TransPerfect to align with stringent auditability requirements.
Although data safety is a key requirement, there are many other factors to take into account, such as responsible AI. Amazon Bedrock Guardrails enabled TransPerfect to build and customize truthfulness protections for the automatic post-edit offering. Large language models (LLMs) can generate incorrect information due to hallucinations. Amazon Bedrock supports contextual grounding checks to detect and filter hallucinations if the responses are factually incorrect or inconsistent. This is a critical feature for a translation solution that requires perfect accuracy.
Harnessing LLMs for automatic post-editing
To translate at scale, Amazon Translate powered machine translation is used in AWS team workflows. Segments whose translations can’t be recycled from translation memories (databases of previous high-quality human translations) are routed to machine translation workflows. Depending on the language or content, Amazon either uses a machine translation-only workflow where content is translated and published with no human touch, or machine translation post-edit workflows. Post-editing is when a linguist finesses the machine-translated output of a given segment to make sure it correctly conveys the meaning of the original sentence and is in line with AWS style guides and agreed glossaries. Because this process can add days to the translation timeline, automating some or all of the process would have a major impact on cost and turnaround times.
The following diagram illustrates the machine translation workflow.
The workflow consists of the following components:
- TM (translation memory) – The translation memory is a client-specific repository of previously translated and approved content. It’s always applied first and maximizes the reuse of existing translations. MT (machine translation) – After existing translations are applied, new content is processed through machine translation using Amazon Translate. APE (automated post-edit) – An LLM is employed to edit, improve, and correct machine-translated content. HPE (human post-edit) – A subject matter expert linguist revises and perfects the machine-translated content.
The following example follows the path through the preceding workflow for one source segment.
| Source | To choose user name attributes, don’t select User name as a sign-in option when you create your user pool. |
| MT | Pour choisir des attributs de nom d’utilisateur, évitez de sélectionner User name (Nom d’utilisateur) comme option de connexion au moment de créer votre groupe d’utilisateurs. |
| APE | Pour choisir des attributs de nom d’utilisateur, évitez de sélectionner User name (Nom d’utilisateur) comme option de connexion lorsque vous créez votre groupe d’utilisateurs. |
| HPE | Pour choisir les attributs de nom d’utilisateur, évitez de sélectionner User name (Nom d’utilisateur) comme option de connexion lorsque vous créez votre groupe d’utilisateurs. |
TransPerfect began working with generative AI and LLMs several years ago with the foresight that AI was on track to disrupt the translation industry. As expected, localization workflows have mostly shifted to “expert in the loop”, and are striving toward “no human touch” models. In pursuit of this, TransPerfect chose to use Amazon Bedrock within its GlobalLink Enterprise solution to further automate and optimize these workflows. Amazon Bedrock, by design, provides data ownership and security. This is a critical feature for TransPerfect clients, especially those in sensitive industries such as life sciences or banking.
With Amazon Bedrock and GlobalLink, machine-translated content is now routed through one of the LLMs available in Amazon Bedrock for automatic post-editing. By using style guides, relevant examples of approved translations, and examples of errors to avoid, the LLM is prompted to improve existing machine translations. This post-edited content is either handed off to a linguist for a lighter post-edit (a less difficult task) or is applied in “no human touch workflows” to greatly improve the output. The result is enhanced quality across the board and the ability for post-editors to focus on higher-value edits.
For post-editing, over 95% of all edits suggested by Amazon Bedrock LLMs showed markedly improved translation quality, leading to up to 50% overall cost savings for translations for Transperfect and freeing human linguists for higher-level tasks.
Harnessing LLMs for transcreation
Although machine translation shows great strength in technical, formal, and instructional content, it hasn’t historically performed as well with creative content that leans into nuance, subtlety, humor, descriptiveness, and cultural references. Creative content can sound stiff or unnatural when machine translated. Because of this, TransPerfect has traditionally relied on human linguists to manually transcreate this type of content.
Transcreation is the process of adapting a message from one language to another while maintaining its intent, style, tone, and context. In German, for example, Nike’s “Just do it” tagline is transcreated to “Du tust es nie nur für dich,” which actually means “you never do it just for yourself.”
A successfully transcreated message evokes the same emotions and carries the same implications in the target language as it does in the source language. The AWS team uses transcreation for highly creative marketing assets to maximize their impact in a given industry. However, transcreation historically hasn’t benefitted from the automation solutions used in other types of localization workflows due to the highly customized and creative nature of the process. This means there has been a lot of interest in using generative AI to potentially decrease the costs and time associated with transcreation.
TransPerfect sought to use LLMs to cut down on time and costs typically associated with transcreation. Rather than an all-human or fully automated process, translations are produced through Anthropic’s Claude or Amazon Nova Pro on Amazon Bedrock, with the prompt to create multiple candidate translations with some variations. Within the translation editor, the human linguist chooses the most suitable adapted translation instead of composing it from scratch.
The following screenshot shows an LLM-powered transcreation within the GlobalLink Translate online editor.
Using GlobalLink powered by Amazon Bedrock for transcreation, users are seeing linguist productivity gains of up to 60%.
Conclusion
Thanks to LLM-powered transcreation and post-editing, customers in industries ranging from life sciences to finance to manufacturing have seen cost savings of up to 40% within their translation workflows and up to an 80% reduction in project turnaround times. In addition, the automatic post-edit step added to machine translation-only workflows provides a major quality boost to the no human touch output.
Amazon Bedrock safeguards data by not allowing sharing with FM providers and excluding it from model improvements. Beyond data security, responsible AI is essential. Amazon Bedrock Guardrails allows TransPerfect to customize truthfulness protections for post-editing. To address AI hallucinations, it offers contextual grounding checks to identify and filter inaccuracies—critical for producing precise translations.
Try out LLM-powered transcreation and post-editing with Amazon Bedrock for your own use case, and share your feedback and questions in the comments.
About the authors
Peter Chung is a Senior Solutions Architect at AWS, based in New York. Peter helps software and internet companies across multiple industries scale, modernize, and optimize. Peter is the author of “AWS FinOps Simplified”, and is an active member of the FinOps community.
Franziska Willnow is a Senior Program Manager (Tech) at AWS. A seasoned localization professional, Franziska Willnow brings over 15 years of expertise from various localization roles at Amazon and other companies. Franziska focuses on localization efficiency improvements through automation, machine learning, and AI/LLM. Franziska is passionate about building innovative products to support AWS’ global customers.
Ajit Manuel is a product leader at AWS, based in Seattle. Ajit heads the content technology product practice, which powers the AWS global content supply chain from creation to intelligence with practical enterprise AI. Ajit is passionate about enterprise digital transformation and applied AI product development. He has pioneered solutions that transformed InsurTech, MediaTech, and global MarTech.
Keith Brazil is Senior Vice President of Technology at TransPerfect, with specialization in Translation Management technologies as well as AI/ML data collection and annotation platforms. A native of Dublin, Ireland, Keith has been based in New York city for the last 23 years.
Julien Didier is Vice-President of Technology for translations.com and is responsible for the implementation of AI for both internal workflows and client-facing products. Julien manages a worldwide team of engineers, developers and architects who ensure successful deployments in addition to providing feedback for feature requests.
Bryan Rand is Senior Vice President of Global Solutions at TransPerfect, specializing in enterprise software, AI-driven digital marketing, and content management strategies. With over 20 years of experience leading business units and implementing customer experience innovations, Bryan has played a key role in driving successful global transformations for Fortune 1000 companies. He holds a BA in Economics from the University of Texas.
