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Stoquart, an language services provider based in Belgium, has acquired Brussels-based ETC Europe, which holds the status of being a translation agency accredited by the European Union and other governmental and international organizations.
The founding managing director of Stoquart Translation Services, Dimitri Stoquart, found contact person ETC Europe General Manager Angelina Janssen due to meetings with the Belgian Association of Translation Companies or BQTA.
He stated that Janssen suggested Stoquart form a consortium with ETC Europe and another language service provider, VerbiVis, to respond to the European Commission's TRAD23 RFP. This resulted in Stoquart achieving second place for English-French translation.
In 2024, he mentioned that Janssen wanted to step back and suggested that Stoquart assume control of ETC Europe. Before the acquisition, shares of ETC Europe were divided among three shareholders; Stoquart has taken over all the shares.
"It was worth joining forces," Stoquart explained. "We have gained both institutional and private clients, along with an increasing number of multilingual projects."
In doing so, ETC Europe further creates new sources of income for Stoquart. The LSP, which now operates as ETC Europe or Stoquart, has recently entered three sizeable contracts with a number of Europe's biggest institutions.
This bodes well for Stoquart, which has faced an accumulated revenue decline of 30% in both 2023 and 2024.
"With this acquisition and the revenues from the European Parliament contract, we will be able to regain our 2022 revenue levels," Stoquart stated.
Strong In-House Resources and Powerful Brands
Stoquart now has around 50 people working for her globally. Janssen will stay until the end of 2024 and will remain available as needed in the near future. (Besides nearly 30 in-house linguists, Stoquart engages between 150-180 freelancers monthly.)
Similar to Version Internationale, ETC Europe holds a strong reputation in the institutional sector. The company will retain its brand identity and limit integration with Stoquart to the essentials required for seamless operations, focusing primarily on activities in the LSP's main office.
Based on Stoquart's location, a big portion of its work is with all variants of French and Dutch, but the company also handles German, Italian, and Spanish. Stoquart now finds itself branching out into other European languages for institutional work, too.
Stoquart's technology approach combines off-the-shelf tools, such as Studio and Phrase, and proprietary tools, including an app that allows users to access several machine translation engines. Stoquart is now expanding into additional European languages for institutional work as well.
Thursday, November 21, 2024
New SAG-AFTRA Game Localization Contract Restricts AI Usage in Dubbing
On November 14, 2024, the Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) announced an updated version of a previous agreement that covers the localization of video game projects produced in a non-English language.
With approximately 160,000 members, SAG-AFTRA calls itself the “world’s largest union representing performers and broadcasters.” The union also represents voiceover artists, including those who provide dubbing.
As comics and gaming website Bleeding Cool reported, the new Independent Interactive Localization Agreement is essentially an updated version of the base terms from the union’s Tiered Budget Independent Interactive Media Agreement, plus AI protections.
The new agreement is signed on a project-by-project basis by employers whose project was originally scripted in a language other than English, and whose intellectual property owner is based outside of the United States.
“Many brilliant, beloved games come to market in the U.S. from other countries, projects which need highly skilled localizing performers,” Interactive Media Agreement Negotiating Committee Chair Sarah Elmaleh was quoted as saying in the press release. Elmaleh added that “[m]any such companies have already signed Interim Localization Agreements”.
The contract was reportedly crafted “based on direct feedback from the community that does this work.”
However, the introduction of the Independent Interactive Localization Agreement does not interrupt an ongoing SAG-AFTRA strike.
The union called for the strike, effective July 26, 2024, in response to stalled negotiations, which began in October 2022. The use of AI still presents a major hurdle, as SAG-AFTRA explained:
“Although agreements have been reached on many issues important to SAG-AFTRA members, the employers refuse to plainly affirm, in clear and enforceable language, that they will protect all performers covered by this contract in their A.I. language.”
The strike applies to over 130 video game projects currently signed to the union’s interim and independent agreements; the goal is for those signatories to sign updated agreements.
Human-Made Recordings and Digital Replicas
The Interactive Media Agreement, originally introduced in 2017, was extended until 2022. At that point, it was replaced by the Tiered-Budget Independent Interactive Media Agreement along with the Interim Interactive Media Agreement and, most recently, the Independent Interactive Localization Agreement.
Katie Sikkema, a union contracts consultant, explained on LinkedIn that the updated agreement “includes a mandatory buyout of all reuse and integration for an additional 50% of scale, which means the IP owner can use the (original, human-made) recordings for whatever they want with no further fees due (except merchandising/talking toys, which must be separately negotiated).”
The contract describes generative AI (GenAI) as a “subset of AI that learns patterns from data and then produces content based on those patterns.”
Interestingly, GenAI is considered separate from “digital replicas” of performers, including voice actors, though the term is not defined. To that point, employers are required to provide the union advance notice if they intend to use GenAI to generate material other than digital replicas.
Employers that want to create GenAI material using prompts including a performer’s name, or a unique character associated with that performer, must get consent from the performer and bargain for the use of GenAI material at a specific minimum rate.
SAG-AFTRA’s strikes have brought attention to the issues GenAI presents for the entertainment industry, an area ripe with opportunities for language AI startups specializing in dubbing. In March 2024, the union ratified the 2023 Television Animation Agreement, which includes “strong protections around the use of artificial intelligence” for voice acting and other performances.
Tuesday, November 12, 2024
Google Says There’s a Better Way to Create High-Quality Training Data for AI Translation
In an October 14, 2024 paper, Google researchers highlighted the potential of AI translations refined by humans or human translations refined by large language models (LLMs) as alternatives to traditional human-only references.
Talking to Slator, Zhongtao Liu, a Software Engineer at Google, explained that their study addresses a growing challenge in the translation industry: scaling the collection of high-quality data needed for fine-tuning and testing machine translation (MT) systems.
With translation demand expanding across multiple languages, domains, and use cases, traditional methods that rely solely on human translators have become increasingly expensive, time-consuming, and hard to scale.
To address this challenge, the researchers explored more efficient approaches to collect high-quality translation data. They compared 11 different approaches — including human-only, machine-only, and hybrid methods — to determine the most effective and cost-efficient one.
Human-only workflows involved either a single human translation step or included an additional one or two human review steps. Machine-only workflows ranged from single-step AI translations using top AI systems — MT systems or LLMs — to more complex workflows, where AI translations were refined by an LLM. Hybrid workflows combined human expertise and AI efficiency; in some cases, AI translations were refined by humans (i.e., post-editors), while in others, human translations were refined by LLMs.
They found that combining human expertise and AI efficiency can achieve translation quality comparable to, or even better than, traditional human-only workflows — all while significantly reducing costs. “Our findings demonstrate that human-machine collaboration can match or even exceed human-only translation quality while being more cost-efficient,” the researchers said.
The best combination of quality and cost appears to be human post-editing of AI translations. This approach delivered top-tier quality at only 60% of the cost of traditional human-only methods, while maintaining the same level of quality.
“This indicates that human-machine collaboration can be a faster, more cost-efficient alternative to traditional collection of translations from humans, optimizing both quality and resource allocation by leveraging the strengths of both humans and machines,” they noted.
The researchers emphasized that the quality improvements stem from the complementary strengths of human and AI collaboration, rather than from the superior capability of either the AI or the human (post-editor) alone, underscoring the importance of leveraging both human and AI strengths to achieve optimal translation quality.
They noted that LLMs were less effective than human post-editors at identifying and correcting errors in AI-generated translations. On the other hand, human reviewers tended to make fewer changes when reviewing human-generated translations, possibly overlooking certain errors. Interestingly, even additional rounds of human review did not substantially improve the quality. This observation supports the argument for human-machine collaboration, where each component helps address the other’s blind spots, according to the researchers.
“These findings highlight the complementary strengths of human and machine post-editing methods, indicating that a hybrid method is likely the most effective strategy,” they said.
Authors: Zhongtao Liu, Parker Riley, Daniel Deutsch, Alison Lui, Mengmeng Niu, Apu Shah, and Markus Freitag
Sijie describes how ECI's initial focus on assisting Western blue-chip companies to enter China evolved into helping Chinese digital titans like Tencent and ByteDance extend their product offerings internationally.
Sijie emphasized the difficulties facing the Asia-Pacific market, where localization maturity in several verticals still lags behind that of the US and Europe. But as sectors like e-commerce, artificial intelligence, and electric vehicles develop, he sees enormous development potential.
Sijie noted that Chinese businesses want to create their own language AI solutions, which makes the industry extremely competitive. This study helps ECI apply state-of-the-art AI developments to client projects.
https://youtu.be/PrJkZEWWCN0
In game localization, Sijie continues to see substantial growth potential. He recognizes that localizing games from China to global markets and vice versa is equally challenging due to differences in player preferences, monetization methods, and cultural contexts.
Sijie shared his thoughts on the financial environment, pointing out that the uncertainty surrounding the adoption of AI in several industries, such as localization, is the reason why market values are currently lower.
Sijie addressed the balance between AI and human localization knowledge in her conclusion, stressing that although AI can increase efficiency, human quality is still preferable in many situations.
Monday, September 23, 2024
Smartcat's Series C Funding, YouTube Dubs Launch, Viva Translate Closes Down
Slator- Language Industry Intelligence
Florian and Esther discuss the language industry news of the week, where they give their impressions from SlatorCon Silicon Valley and touch upon the findings from the 2024 ALC Industry Survey.
In a significant funding update, Esther reports that Smartcat raised USD 43m in a Series C round, bringing their total funding to USD 70m. This funding will support product innovation in AI translation and multilingual content generation.
Florian talks about YouTube’s potential launch of AI dubbing, a feature in testing that aims to generate translated audio tracks for videos, significantly enhancing content accessibility and engagement.
The duo bid farewell to publicly traded Keywords Studios, which is delisting after being acquired by private equity firm EQT. They also note the shutdown of Viva Translate, a speech-to-speech translation company that will open-source its tools as it winds down.
On September 5, 2024, more than 150 language industry and technology leaders gathered at Hotel Nia in Menlo Park, Silicon Valley.
The event offered a friendly and relaxed environment, encouraging networking and reconnections among participants. Attendees from over a dozen countries and four continents emphasized the importance of in-person Slator events in addition to virtual ones. The expo hall was also buzzing with activity.
Esther Bond, Head of Advisory at Slator, kicked off the event with a warm welcome, outlining the day's presentations and panels, and encouraging delegates to network and engage with each other.
Key Takeaways from SlatorCon Silicon Valley 2024
Florian Faes, Managing Director of Slator, opened the sessions by presenting key insights from Slator’s latest research on the language industry's current state. He discussed practical applications of large language models (LLMs) in localization workflows and shared predictions for the next few years.
RWS took the stage for the first presentation, with Vasagi Kothandapani and Mark Lawyer discussing the diversification of services into AI solutions. They emphasized the role of content as a driving force for digital transformation, business innovation, enhancing customer experience, corporate growth, global engagement, and market evolution.
Key Takeaways from SlatorCon Silicon Valley 2024
The day's first panel, moderated by Esther Bond, focused on investment strategies.
Andrew Doane of K1 Investment Management and Aditya Govil from VSS Capital Partners explored the influence of AI on the language technology sector, with particular emphasis on the healthcare and B2B SaaS industries. They also discussed the role of private equity in the language tech space and shared insights on strategic considerations for investments and acquisitions.
Helena Batt, who oversees localization operations for the TEDConferences, took the podium next to provide unique insights on the organization’s implementation of AI dubbing for TED Talks. Among the technical challenges encountered, Batt mentioned preserving vocal characteristics and emotional nuance, and achieving seamless lip sync.
Betting on Technology
The Language AI Stack panel, moderated by Anna Wyndham, Slator's Head of Research, featured insights from Georg Ell of Phrase and Hameed Afssari of Uber. They discussed AI as a technology stack, focusing on the practical applications of large language models (LLMs) in localization, including machine translation (MT), workflow optimization, and managing linguistic assets.
A second technology panel, led by Florian Faes, explored the interpreting field. Oddmund Braaten from Interprefy, Fardad Zabetian from KUDO, and Jeremy Woan from CyraCom International shared their perspectives on how automation transforms interpreting services.
Another panel, moderated by Alex Edwards, Slator Senior Research Analyst, offered insights on localization systems integration, global 24/7 services, and enterprise program management. Panelists included Pavel Soukenik from Acolad, Nitin Singhal from SnapLogic, and Agustín Da Fieno Delucchi from Microsoft.
Silvio Picinini from eBay Localization delivered a thought-provoking presentation, exploring two scenarios: applying AI to existing localization processes or reimagining those processes entirely, and the potential outcomes of each approach.
Florian Faes concluded the event with closing remarks, inviting attendees to join SlatorCon Remote in November 2024 or meet in person again at SlatorCon London in 2025. More detailed follow-up coverage is forthcoming.
Friday, August 23, 2024
Researchers Combine DeepL and GPT-4 to Automate (Research) Questionnaire Translation
In today's fast-paced research environment, the demand for accurate and swift translation of research questionnaires is higher than ever. With global collaborations becoming the norm, researchers are constantly on the lookout for efficient ways to break language barriers. Enter the world of AI, where tools like DeepL and GPT-4 are changing the game. But how exactly does combining these technologies automate research questionnaire translation, and why is it a big deal? Let's dive in.
Slator- Language Industry Intelligence
The Role of DeepL in Translation
Overview of DeepL
DeepL has quickly risen to prominence as one of the most reliable AI-powered translation tools available. Known for its impressive accuracy, DeepL has been widely adopted by professionals who need translations that go beyond the literal, capturing the essence and context of the source material.
Advantages of Using DeepL for Translation
The primary strength of DeepL lies in its ability to understand context. Unlike traditional translation tools that may deliver clunky or out-of-context translations, DeepL leverages deep learning algorithms to provide translations that are more natural and nuanced. This makes it particularly valuable in the field of research, where the precise meaning of each question is critical.
Understanding GPT-4
Introduction to GPT-4
GPT-4, the latest iteration in OpenAI's Generative Pre-trained Transformer series, is a language model designed to understand and generate human-like text. Its capacity to grasp complex large language model (LLM) and produce coherent responses makes it a powerful tool in various applications, including translation.
How GPT-4 Enhances Language Understanding
What sets GPT-4 apart is its ability to understand and generate text with a high degree of fluency. It can process a wide range of languages and adapt to different linguistic contexts, making it an invaluable partner in translation tasks. This adaptability is crucial when dealing with research questionnaires that often contain specialized terminology and nuanced language.
Combining DeepL and GPT-4 for Research Translation
Why Combine DeepL and GPT-4?
While both DeepL and GPT-4 are powerful on their own, combining them creates a synergy that enhances the overall translation process. DeepL's contextual accuracy pairs well with GPT-4's ability to generate coherent and contextually appropriate text, leading to translations that are both accurate and natural.
The Synergy Between DeepL and GPT-4
When used together, DeepL can provide the initial translation, which GPT-4 can then refine. This collaboration allows for more precise and culturally sensitive translations, which are particularly important in research, where misinterpretations can lead to skewed data.
Benefits of Automating Research Questionnaire Translation
Speed and Efficiency
One of the most significant advantages of automating research questionnaire translation is the speed at which it can be done. What used to take weeks or even months can now be accomplished in a matter of hours, allowing researchers to focus more on analysis and less on administrative tasks.
Cost-Effectiveness
Automating the translation process also cuts down on costs. By reducing the need for human translators, especially for initial drafts, organizations can allocate resources more effectively.
Improved Accuracy
AI-driven translation tools, especially when combined, can achieve a level of accuracy that minimizes the risk of errors. This is particularly important in research, where the integrity of data relies heavily on the clarity and precision of the questions asked.
Case Studies of Automated Translation in Research
Real-World Applications
Several organizations have already begun integrating AI into their research translation processes. For instance, a leading university in Europe used the DeepL and GPT-4 combination to translate questionnaires for a multi-country study on public health. The results were impressive, with over 90% accuracy in the translations, far surpassing traditional methods.
Success Stories
Another success story comes from a non-profit organization that conducts surveys in various languages. By automating their translation process, they not only sped up data collection but also improved the quality of their translations, leading to more reliable survey results.
Challenges in Automating Research Questionnaire Translation
Language Nuances
One of the primary challenges in automating translation is dealing with language nuances. AI tools must be able to understand idiomatic expressions, slang, and cultural references, which can be difficult for even the most advanced systems.
Contextual Understanding
Ensuring that the context is preserved during translation is another hurdle. Research questionnaires often contain complex ideas that need to be conveyed accurately in another language. Misinterpretation of these ideas can lead to data that is not only inaccurate but also unusable.
Ethical Considerations
As with any AI application, there are ethical considerations to keep in mind. Issues like data privacy, the potential for bias in translations, and the role of human oversight are all critical factors that need to be addressed.
Overcoming Challenges with AI Technology
Advances in AI for Better Translation
Recent advancements in AI are helping to overcome many of the challenges associated with automated translation. For example, improvements in natural language processing (NLP) are allowing AI systems to better understand context and nuances, leading to more accurate translations.
Role of Human Oversight
While AI can significantly enhance the translation process, the role of human oversight cannot be understated. Human translators can provide the necessary context and cultural understanding that AI might miss, ensuring that the final translation is both accurate and culturally appropriate.
Future of AI in Research Translation
Emerging Trends
The future of AI in research translation looks promising, with trends pointing towards even more sophisticated and reliable tools. As AI continues to evolve, we can expect to see greater integration of these technologies in research, leading to faster and more accurate data collection.
Potential Impact on Global Research
The global impact of AI-driven research translation cannot be overstated. By breaking down language barriers, AI has the potential to democratize research, making it more accessible to non-English speaking populations and fostering greater collaboration across borders.
Conclusion
In conclusion, the combination of DeepL and GPT-4 represents a significant advancement in the field of research translation. By automating the translation process, researchers can save time, reduce costs, and improve the accuracy of their data. While challenges remain, the future looks bright, with AI poised to play an increasingly important role in global research.
