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Red Cross volunteers getting ready to deliver the mission. Photo illustration by Juan Carlos Molina Padilla/American Red Cross
The American Red Cross Multicultural Communications team announces the launch of its innovative Translator Hub. This cutting-edge platform is designed to bridge the language gap and ensure effective communication with non-English speaking audiences. By utilizing a custom machine translation system complemented by rigorous human reviews, the hub offers accurate and culturally sensitive translations for all text and materials.
The Translator Hub is a significant milestone for the American Red Cross, reflecting its commitment to inclusivity and accessibility. Users within the organization can now translate a wide range of documents, ensuring vital information reaches diverse communities. Whether it’s press releases, social media content, or educational materials, the hub ensures these messages are understood by everyone, regardless of language barriers.
The key to the hub’s success is its dual approach to translation. Initially, a custom machine translation system generates a preliminary document. This is then meticulously reviewed by human translators to guarantee accuracy, contextual relevance and cultural appropriateness. This two-step process ensures that the final output is not only linguistically correct, but also resonates with the target audience.
The need for efficient and effective localization has never been more critical to global success. Organizations operate in an increasingly interconnected world – one that offers immense opportunities but also suffers from an explosion in content.
Traditional localization isn’t built for the scale and pace needed by global enterprises today so struggles to keep up with the ever-growing demand for multilingual content. Each efficiency gain, like translation memory or machine translation, seems to be quickly eclipsed by the sheer volume of content requiring translation. Businesses are often forced to choose between cost, quality and speed. Known as the ‘iron triangle’, it’s long been an insurmountable obstacle to quickly delivering high-quality translations at scale.
To overcome this conundrum, RWS has developed Evolve, its flagship Human+AI solution – building on the philosophy of ‘Genuine Intelligence’ – that aims to redefine what’s possible in the world of localization.
In a June 27, 2024 paper, researchers from Unbabel and Instituto de Telecomunicações introduced xTOWER, a large language model (LLM) designed to generate “high-quality” explanations for translation errors and use them to suggest improved translations.
The researchers explained that machine translation (MT) systems, despite their strong performance, often produce translations with errors. “Understanding these errors can potentially help improve the translation quality and user experience,” they said.
Built on top of TOWERBASE — an LLM designed, trained, and optimized for MT-related tasks —, xTOWER offers detailed, human-readable explanations for translation errors and suggests corrections based on this analysis.
Specifically, the process involves inputting a source text and its translation into xCOMET, which annotates the translation with error spans and assigns a quality score. The complete input (i.e., the source text and its translation), the annotated translation, and the quality score are then passed to xTOWER, which generates explanations for each error span and proposes a new corrected translation based on these explanations.
In a June 12, 2024 paper researchers from Tencent AI and the Harbin Institute of Technology introduced TasTe, a method for teaching large language models (LLMs) to translate through self-reflection.
The key idea is to enable LLMs to generate preliminary translations (i.e., drafts), self-evaluate their own translations, and make refinements based on the evaluation.
The researchers explained that LLMs have shown exceptional performance across various natural language processing tasks, including machine translation (MT). However, their translations still do not match the quality of supervised neural machine translation (NMT) systems.
To address this, the authors proposed the TasTe framework (translating through self-reflection), which improves the translation capabilities of LLMs by incorporating a self-reflection process.
Google LLC said today that it’s bringing 110 new languages to its web and smartphone translation app using the power of artificial intelligence, making it more comprehensive than ever with 243 languages in total.
This is the largest expansion to date for Google Translate since 2022, when the company brought 24 new languages to the app using zero-shot machine translation. That’s where a language model learns to translate a language without ever seeing an example.
The company employed PaLM 2, a transformer-based large language model AI developed by Google Research that first powered Bard, Google’s AI chatbot. It eventually evolved into Gemini, which is now powered by the company’s AI model of the same name. Differing from Gemini, PaLM 2 was trained on Pathways, a vast dataset of human language containing more than 1.56 trillion words and 250 billion parameters.
Given the size of this dataset, Google said, PaLM 2 can attain unprecedented fluency with written languages and demonstrated an impressive ability to perform linguistic tasks during testing including understanding idiomatic phrases. However, unlike Gemini, PaLM 2 cannot understand or generate images or work with audio.
Copenhagen, Denmark – June 27 2024 – EasyTranslate, an AI-powered language operations platform, has launched HumanAI, designed to transform the translation industry.
The translation market has long been split into two segments: technology providers and service providers. This separation has created a gap, as there has not been a single solution that combines both. The rise of the LangOps Platform addresses this issue by integrating services and technology into one cohesive solution, eliminating the need for separate TMS and service providers. This innovation fills a crucial gap in the market, streamlining multilingual content management and service delivery in one cohesive solution – Language Operations Platform.
HumanAI combines advanced AI models with strategic human intervention to deliver unparalleled quality, speed and cost-effectiveness, making it a powerful tool for businesses looking to improve on-brand global communication.
In the latest edition of Google’s SEO Office Hours video series, a company representative gave an update on the search giant’s take on AI-generated translations for multilingual websites.
The series, led by Google’s Search Relations Team Lead, John Mueller, answers questions from users about website crawling, indexing, and internationalization.
One user asked, “how can one be transparent in the use of AI translations without being punished for AI-heavy content?” Google’s Mueller responded that while there is “no special markup” to label webpages as automatic translations, users should “consider whether translated pages align with the quality bar that you set for yourself.”
Mueller suggested that if the translated content isn’t high quality, users would be better off not indexing the pages for search engines. “Ultimately, a good localization is much more than just a translation of words and sentences, so I would definitely encourage you to go beyond the minimal bar if you want users in other regions to cherish your site”, he concluded.
It will soon be easier to see Facebook and Instagram posts in lesser-spoken global languages, but an expert suggests that to improve the tool Meta should talk to native speakers.
It will soon be easier to see Facebook and Instagram posts in 200 lesser-spoken languages around the world.
Meta’s No Language Left Behind (NLLB) project announced in a paper published this month that they’ve scaled their original technology.
The project includes a dozen “low resource” European languages, like Scottish Gaelic, Galician, Irish, Lingurian, Bosnian, Icelandic and Welsh.
According to Meta, that’s a language that has less than one million sentences in data that can be used.
Experts say that to improve the service, Meta should consult with native speakers and language specialists as the tool still needs work.
In a June 4, 2924 paper researchers from Google Research and Google DeepMind presented SimulTron, a model designed for on-device, real-time speech-to-speech translation (S2ST) built upon the Translatotron architecture.
The researchers highlighted the ongoing evolution of S2ST technology while emphasizing the persistent challenge of achieving accurate, real-time, on-device simultaneous translation. They noted that existing simultaneous translation models are not adequately optimized for the unique constraints of mobile devices, underscoring the significant challenge of achieving seamless real-time translation on mobile devices.
“Today, with smartphones and tablets being central hubs for personal and professional interactions, on-device S2ST is crucial,” they said.
In response to this challenge, they introduced SimulTron, a model that leverages the strengths of Translatotron while incorporating key modifications specifically tailored for the on-device, simultaneous translation scenarios.
Earlier this year, a drumbeat of news headlines played into public anxieties about the safety of human jobs when Duolingo, a language learning app, became a prominent example of a company cutting workers and replacing them with artificial intelligence.
The most eye-catching job cuts were those for translators, who worked on some of the company’s less popular language education courses. Translators and interpreters are often near the top of media listicles as the jobs most likely to be killed by AI. When the stories about Duolingo’s job cuts circulated, they seemed to confirm that the inevitable AI jobs apocalypse had arrived.
In a recent conversation with Planet Money, the CEO of Duolingo, Luis von Ahn, downplayed the meaning of the cuts. It wasn’t full-time employees. It was only 10% of their contractors. His company’s recent embrace of generative AI only played one part in the decision, and so on. More interesting, considering Duolingo’s official partnership with OpenAI, was von Ahn’s reaction to the company’s recent demonstration of its newest version of ChatGPT, GPT-4o.
ORLANDO, FL — Even if the equipment is confiscated or destroyed, if the unthinkable happens, the translation work -weeks or months of translation progress- is protected. Wycliffe Associates provides translators living in remote regions with the necessary technology to protect and accelerate the biblical translation.
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ORLANDO, FL — Even if the equipment is confiscated or destroyed, if the unthinkable happens, the translation work -weeks or months of translation progress- is protected. Wycliffe Associates provides translators living in remote regions with the necessary technology to protect and accelerate the biblical translation.
Wycliffe Associates, one of the world’s leading bible translation organizations, provides translators living in remote regions with the necessary technology to protect and accelerate the translation of scriptures.
The organization announced the development of bible translation acceleration kits for translators living in isolated areas without internet connectivity.
In the last few years, technology has allowed the bible to reach millions of people in new and innovative ways.
Bill McDonald – Regional Development Director:
At Wycliffe Associates, we accelerate Bible translation work by empowering national translators to provide the Word of God in their own language. We partner with the local church to guide and safeguard that translation, and then involve people from around the world to provide resources, technology, training, and especially support for Bible translation.
Wycliffe Associates, one of the world’s leading bible translation organizations, provides translators living in remote regions with the necessary technology to protect and accelerate the translation of scriptures.
The organization announced the development of bible translation acceleration kits for translators living in isolated areas without internet connectivity.
In the last few years, technology has allowed the bible to reach millions of people in new and innovative ways.
In early January 2024, when many in the language industry were likely pondering how to eventually incorporate AI into their offerings or processes, OpenAI announced its GPT Store. Back then, a few translation GPTs could be found, including one built by Phrase called “Phrase Expert.”
By the time OpenAI launched the store to the general public, there were already more than three million GPTs done by pre-release testers. After the kind of hype seen in late 2022 with ChatGPT and all the drama surrounding the company’s CEO and Board in 2023, the store launch was also a popular subject in the news and on social media.
Then, the announcement of the company’s text-to-video generator, Sora, arrived in February 2024, lest people get OpenAI out of their minds for too long.
In March 2024, we asked readers if they had ever used Custom GPTs since the store was launched, and over two-thirds of respondents (64.6%) said No. Over a combined quarter of readers said they tested GPTs a bit after launch (14.6%) or from time to time (12.5%), and a very small percentage said they have been using them daily (8.3%).
SAN FRANCISCO, June 10, 2024 — Unbabel, an AI-powered Language Operations (LangOps) platform that helps businesses deliver multilingual customer experience at scale, recently announced the launch of TowerLLM, the first Large Language Model (LLM) designed, trained, and optimized for translation, resulting in the best performing translation LLM commercially available. Unbabel customers using TowerLLM will significantly improve machine translation accuracy, reducing errors and cost, and will benefit from a more cost-effective price than popular LLMs.
TowerLLM provides superior translation quality to leading LLMs like GPT-4o and GPT-3.5, while consistently outperforming Google and DeepL, because it was made from scratch to be multilingual. Built on a large public data set, then trained only on best-quality translation data filtered out and curated by Unbabel’s quality LLM, COMETKiwi, TowerLLM demonstrates that LLMs are the next step in the evolution of AI translation.
“Despite the doubts from some in the industry, TowerLLM clearly demonstrates that LLMs are the best solution for machine translation,” said João Graça, Co-Founder and Chief Technology Officer at Unbabel. “We’ve invested years into developing LLMs, so we’re not surprised to see TowerLLM outperform GPT-4o. This is the second multilingual LLM we’ve released after COMETKiwi, and we’re just getting started.”
In the last 18 months, large language models (LLMs) have fueled a raft of startups and been rapidly injected into language technology platforms. But how have professional linguists responded to this pivotal moment in AI?
A survey of more than 750 translators — conducted in April 2024 for the 2024 Slator Language Industry Market Report — found that AI adoption by professional linguists is now widespread. Two-thirds of translators have used AI in their translation work in the past 12 months.
Whether via AI models (e.g., ChatGPT, Gemini), translation management systems, or standalone AI platforms, linguists are actively leveraging AI in new and creative ways to enhance quality and efficiency.
Here, we present the top 10 applications of AI by translators, based on results from Slator’s 2024 linguist survey.
Language is one the greatest gifts our world has ever received. Through language we’re able to communicate with each other and also express our uniqueness.
Languages are a way of seeing things. The way we explain different concepts – or whether the concepts exist in our language at all – provides a vital window to our culture. As Microsoft President Brad Smith put it: “When a community loses a language, it loses its connection to the past – and part of its present. It loses a piece of its identity.”
Te reo Māori is part of Aotearoa’s identity. When Kiwis travel, we communicate with others in English, the international language of business. But when we share what makes us unique, what do we talk about? Thanks to the success of the All Blacks, people know us for the haka. We’re also known for being “Kiwi”, for our celebrities such as Taika Waititi and places like Rotorua, Kaikoura and Waiheke. What all of these people, places and things have in common is a language – te reo Māori.
Growing up, I wasn’t able to express myself in the language of my people, something that’s helped shape who I am and what I do as the founder of a translation technology company today. Not only do we have to be open to using te reo Māori, we have to get smarter about harnessing technology to ensure more New Zealanders have an opportunity to engage with it as they go about their day.
The World Economic Forum is predicting that within the next 100 years, around 1,500 languages could disappear forever. The research found that regionally dominant languages taught in class often overshadow indigenous tongues. Unless they’re used regularly and encouraged to become a part of people’s daily lives, these foundations of our culture get lost. Without them, we lose so much more than just words – we lose what makes us uniquely us.
Are language service providers (LSPs) seizing the benefits offered by generative AI and large language models? An April 2024 Slator study has found that the answer is a resounding “yes”.
The survey of AI adoption across 223 companies found that one in three LSPs have implemented large language models into their workflows. Furthermore, close to 20% of companies have now tested use cases or beta versions.
Just 3% of LSPs told Slator that they have no plans to implement LLMs.
Beyond performing the core machine translation task, LLMs — such as Meta’s Llama 3 and OpenAI’s GPT 4 and 4o — have significant potential to augment and enable language AI services. Key applications include quality estimation, dynamic translation adaptation, and multilingual text generation, among others.
Gabriel Fairman, CEO of Bureau Works, speaking at the opening of SlatorCon London on Wednesday 21 May, emphasized that LLMs represent nothing less than “a new way for humans to relate to machines”.
In a May 16, 2024 paper, Jared Coleman, Bhaskar Krishnamachari, and Khalil Iskarous from the University of Southern California, along with Ruben Rosales from California State University introduced a new approach for machine translation (MT) that is “particularly useful” for no-resource languages, which lack publicly available bilingual or monolingual corpora.
This approach, named LLM-RBMT (LLM-Assisted Rule-Based Machine Translation), combines the strengths of large language models (LLMs) and rule-based machine translation (RBMT) techniques.
The researchers highlighted the exceptional capabilities of LLMs in MT but noted their limitations in low-resource or no-resource language scenarios. “There have been many efforts in improving MT for low-resource languages, but no-resource languages have received much less attention,” they said.
Despite the perception of RBMT as a “relic of the past”, the researchers emphasized ongoing research and development in RBMT systems tailored for under-resourced languages.
Japanese manga localization tech company Orange raised JPY 2.2bn (USD 19.5m) in what they called a “pre-Series A” financing, the company announced in a May 6, 2024 press release.
Manga publisher Shogakukan led the financing round. Other participants included the Japanese government’s Japan Industrial Innovation Investment Corporation, Globis Capital Partners, ANRI, SBI Investment, JIC Venture Growth Investments, Miyako Capital, Chiba Dojo Fund, Mizuho Capital, Mitsubishi UFJ Capital, and GFR Fund.
Orange, founded in 2021, uses computer vision and NLP, including AI translation, to translate manga titles in a fraction of the time required by traditional methods.
The company claims that it can translate, from Japanese to English, up to 500 manga titles per month — a figure Orange pegs at about five times the current industry rate.
The company’s ultimate goal: translate 50,000 manga titles in the next five years. Orange will use some of the funds raised in this round to launch a digital manga store called emaqi in summer 2024. The store will reportedly feature recommendations from manga influencers — as well as AI-generated suggestions.
It will also support AI dubbing and subtitles on LinkedIn, Coursera, Bloomberg, and more.
Microsoft Edge will soon offer real-time video translation on sites like YouTube, LinkedIn, Coursera, and more. As part of this year’s Build event, Microsoft announced that the new AI-powered feature will be able to translate spoken content through both dubbing and subtitles live as you’re watching it.
So far, the feature supports the translation of Spanish into English as well as the translation of English to German, Hindi, Italian, Russian, and Spanish. In addition to offering a neat way to translate videos into a user’s native tongue, Edge’s new AI feature should also make videos more accessible to those who are deaf or hard of hearing.
Edge will also support real-time translation for videos on news sites such as Reuters, CNBC, and Bloomberg. Microsoft plans on adding more languages and supported websites in the future.
The German startup has been rivalling giants such as Google Translate
DeepL, the Cologne-based unicorn that has been rivalling machine translation giants such as Google Translate, today announced a new investment of $300mn (€277mn), reaching a $2bn valuation.
Born from online dictionary Linguee, the startup was founded in 2017 by Jarek Kutylowski, who’s also serving as the company’s CEO.
DeepL offers free and premium AI translation services, with a special focus on B2B products, where the company sees the biggest demand.
It now covers 32 languages and counts 100,000 business users. A list that includes the likes of Nikkei, Coursera, and Deutsche Bahn.
The investment follows a period of significant growth for DeepL. Within the last 12 months, the startup has opened its first office in the US and added Arabic, Norwegian, and Korean to its language portfolio. It has also launched new products for businesses, such as the DeepL Write Pro AI assistant.
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United States
I read the daily digest of ProZ.com translation news to get the essential part of what happens out there!
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