Landexx, a language services provider based in Germany, has filed for bankruptcy.

According to a court filing reported by several German legal aggregation sites, the language services provider Landexx has filed for bankruptcy.

Information on the LSP is now difficult to find, as Landexx's website appears to be down. Additionally, as a private company, Landexx’s annual financial reports are not publicly accessible.

German Language Services Provider Landexx Files for Bankruptcy

Landexx, led by Managing Director Christel Stemmer, provided various services, including translation, interpreting, language training, and desktop publishing.

Language professionals have informed colleagues of the news. For example, German-English translator Jill Sommer advised freelancers in a blog post to contact the bankruptcy trustee, Stephan Höltershinken, about any unpaid invoices. She also warned others not to accept translation assignments from the company.

For at least a few years before the LSP's bankruptcy filing, there had been rumblings about Landexx among freelancers online.

“They were an excellent client, always very professional, EXCEPT that I always had to chase late payments. They often paid 3 to 4 months late,” read a November 2020 complaint by one translator who said she began working with Landexx in 2009.

The freelancer explained that she had to "chase payments by email and phone " and wait a year, even hiring a lawyer, to receive payment for six outstanding invoices.

"[T]hey have told my lawyer that they will no longer be sending me any work, even though I did nothing wrong," she added. "I believe other freelance translators must be in the same position."

Another translator wrote on Reddit in May 2024 that they were pursuing legal action against Landexx through a court order for payment "[a]fter countless reminder e-mails that have been ignored."

Landexx also had a mixed reputation on the job board ProZ, where the LSP’s "Blue Board affiliation" — based on freelancers’ willingness to work with the company again — stood at two out of five stars. A December 2022 staff note indicated that "[t]his outsourcer has been banned from posting jobs at ProZ.com." Details regarding Landexx’s specific case were not disclosed.

“Use of the site dishonestly or fraudulently will result in termination of site use and associated privileges,” states ProZ’s termination policy, which also specifies that ProZ "reserves the right to refuse access to this site to any party without giving a reason."

Humanless LSP as a Fun Weekend Project

Florian and Esther discuss the language industry news of the week, giving a recap of SlatorCon London and exploring some use cases from the Slator Pro Guide: Language AI for Consumers.

Florian talks about Andrew Ng’s recent project on agentic machine translation, which involves using large language models (LLMs) to create a virtual language service provider (LSP).

The duo touched on Apple’s recent Worldwide Developer Conference, where Apple Watch is set to get a translation widget and also recently announced a new translation API.

Florian shares RWS’s half-year financial results, where despite declines in revenue, the company’s stock rose by 20%, likely due to investor perception of AI-enabled services and new product offerings like Evolve and HAI gaining traction.

Esther talks about DeepL’s USD 300m funding round, which valued the company at USD 2bn, a testament to the growing interest in AI models. She also covers Unbabel’s launch of TowerLLM, which claims to outperform competitors like Google Translate and DeepL.

In Esther’s M&A corner, Keywords Studios eyes a GBP 2.2bn deal from Swedish private equity firm EQT, Melbourne LSP Ethnolink buys Sydney-based competitor Language Professionals, and ZOO Digital acquires Italian dubbing partner LogoSound.

Esther gives a nod to the positive financial performances of companies like ZOO Digital and AMN’s language services division, with more mixed results for Straker.

Here’s a New Dataset for Emotion-Aware Speech Translation

Imagine a world where translations don't just convert words but also capture the emotions behind them. This is the promise of MELD-ST, a new dataset introduced in May 2024 by researchers from the Technical University of Munich, Kyoto University, SenseTime, and Japan's National Institute of Informatics. This dataset is designed to revolutionize speech translation by ensuring that emotional context is preserved, enhancing both speech-to-text (S2TT) and speech-to-speech translation (S2ST) systems.

Background

Emotion plays a critical role in human conversation, yet most translation systems struggle to accurately convey the emotional tone of the original speech. While text-to-text translation (T2TT) has seen some progress in emotion-aware translation, speech translation remains a largely uncharted territory. The introduction of MELD-ST aims to fill this gap.

The Creation of MELD-ST

MELD-ST builds upon the existing Multimodal EmotionLines Dataset (MELD), which features dialogues rich in emotional content. By adding corresponding speech data from the TV series "Friends," MELD-ST offers audio and subtitles in English-to-Japanese and English-to-German language pairs. This dataset includes 10,000 utterances, each annotated with emotion labels, making it a valuable resource for studying emotion-aware translation.

Features of MELD-ST

What sets MELD-ST apart is its inclusion of emotion labels for each utterance, allowing researchers to conduct detailed experiments and analyses. The dataset features acted speech in an emotionally rich environment, providing a unique resource for initial studies on emotion-aware speech translation.

The Significance of Emotion in Translation

Consider the phrase "Oh my God!" Its translation can vary significantly based on the emotional context—surprise, shock, excitement. Accurately translating such phrases requires an understanding of the underlying emotions to ensure the intended intensity and sentiment are preserved, which can differ across cultures.

Technical Details of MELD-ST

MELD-ST comprises audio and subtitle data with English-to-Japanese and English-to-German translations. Each utterance is annotated with emotion labels, enabling researchers to explore the impact of emotional context on translation performance.

Research Methodology

The researchers tested MELD-ST using the SEAMLESSM4T model under various conditions: without fine-tuning, fine-tuning without emotion labels, and fine-tuning with emotion labels. Performance was evaluated using BLEURT scores for S2TT and ASR-BLEU for S2ST, along with metrics such as prosody, voice similarity, pauses, and speech rate.

Findings on S2TT

Incorporating emotion labels led to slight improvements in S2TT tasks. The researchers observed that fine-tuning the model improved the quality of translations, with BLEURT scores indicating better alignment with the emotional context of the original speech.

Findings on S2ST

However, for S2ST tasks, fine-tuning with emotion labels did not significantly enhance results. While fine-tuning improved ASR-BLEU scores, the addition of emotion labels did not yield notable benefits. This highlights the complexity of accurately conveying emotions in speech translations.

Challenges and Limitations

The study faced several limitations. The use of acted speech, while useful, may not fully represent natural conversational nuances. Additionally, the dataset's focus on a specific TV series limits the diversity of speech contexts. Future research should address these limitations and explore more natural speech settings.

Future Directions

To advance emotion-aware translation, researchers propose several strategies. These include training multitask models that integrate speech emotion recognition with translation, leveraging dialogue context for improved performance, and refining datasets to encompass more varied and natural speech environments.

Access and Availability

MELD-ST is available on Hugging Face and is intended for research purposes only. Researchers and developers can utilize this dataset to explore and enhance emotion-aware translation systems.

Conclusion

MELD-ST represents a significant step forward in the field of speech translation, offering a valuable resource for incorporating emotional context into translations. While initial results are promising, continued research and development are essential to fully realize the potential of emotion-aware translation systems.

Phrase CEO Georg Ell on the Arms Race in Language Technology

Georg Ell, CEO of Phrase, returns to SlatorPod for round two to talk about the accelerating adoption of generative technologies and AI. In this episode, he delves into the broader implications of AI, focusing on the transformative potential of language technology in business. Let’s explore the insights shared by Georg Ell and understand how Phrase is navigating this rapidly evolving landscape.

Georg Ell: A Visionary Leader in Language Technology

Georg Ell is a prominent figure in the language technology sector. With a rich background in technology leadership, he has spearheaded various initiatives aimed at integrating advanced AI into language solutions. His journey with Phrase has been marked by a commitment to innovation and a vision to push the boundaries of what language technology can achieve.

The Accelerating Adoption of Generative Technologies and AI

The adoption of AI and generative technologies in language solutions is accelerating at an unprecedented pace. Businesses are recognizing the immense potential of these technologies to revolutionize how they manage and utilize language data. From automated translations to real-time language processing, AI is becoming a cornerstone of modern language solutions.

Broader Implications of AI in Business

AI's impact extends far beyond mere translation improvements. Business leaders are increasingly focusing on the cost benefits, return on investment (ROI), and time-to-value benefits that AI brings. By automating routine tasks, AI allows businesses to allocate resources more efficiently, leading to significant cost savings and faster implementation times.

Beyond Translation: Hyperautomation, Hyperpersonalization, and Hyperscale

Georg Ell emphasizes that the true power of AI in language technology lies in its ability to enable hyperautomation, hyperpersonalization, and hyperscale.

Hyperautomation

Hyperautomation involves the use of AI to automate complex business processes that traditionally required human intervention. By leveraging AI, businesses can streamline operations, reduce errors, and enhance productivity.

Hyperpersonalization

In today’s competitive landscape, personalized customer experiences are crucial. Hyperpersonalization uses AI to tailor interactions based on individual preferences and behaviors, creating a more engaging and relevant experience for customers.

Achieving Hyperscale

Hyperscale refers to the ability to scale operations rapidly and efficiently. AI-driven language solutions allow businesses to manage large volumes of multilingual content, ensuring consistency and quality across all communications.

Demand for Enterprise-Grade Multilingual Content Solutions

Despite the advancements in AI, there remains a strong demand for enterprise-grade solutions capable of generating multilingual content at scale. Businesses require robust, reliable technology to meet their global communication needs. Phrase is addressing this demand with its suite of advanced language solutions designed for enterprise use.

Phrase’s New Product Launches

Phrase continues to innovate with new product launches aimed at enhancing translation quality and efficiency. One of the standout offerings is Next GenMT, a cutting-edge machine translation technology.

Next GenMT: Combining GPT-4o with Phrase’s MT Engine

Next GenMT is a revolutionary product that combines the power of GPT-4o with Phrase’s proprietary MT engine. This fusion enhances translation quality and efficiency, delivering superior results compared to traditional machine translation methods.

Features and Benefits

Next GenMT offers a range of features designed to improve translation workflows. It provides more accurate translations, faster processing times, and greater flexibility for handling diverse content types.

Impact on Translation Quality and Efficiency

By integrating advanced AI with Phrase’s robust MT engine, Next GenMT significantly boosts translation quality. It reduces the need for post-editing and ensures that translations are contextually accurate and linguistically sound.

https://youtu.be/vdBndWUi-6g

Auto LQA: AI-Driven Language Quality Assessment

Phrase’s Auto LQA is another innovative solution designed to improve language quality assessment processes. This AI-driven tool assists linguists by automating the evaluation of translation quality, thereby reducing costs and time spent on quality checks.

Purpose and Functionality

Auto LQA uses sophisticated algorithms to assess translations against predefined quality metrics. It identifies errors and inconsistencies, providing detailed feedback to linguists for refinement.

Benefits for Linguists and Businesses

Auto LQA not only enhances the efficiency of linguists but also ensures that businesses maintain high-quality standards across their multilingual communications. It enables quicker turnaround times and reduces the burden of manual quality assessments.

Phrase’s Strategic Shift to a Platform-Centric Company

In a strategic move, Phrase is transitioning from being a product-centric company to a platform-centric one. This shift allows Phrase to offer a comprehensive suite of capabilities that cater to the diverse needs of its clients.

Transition from Product to Platform

The platform-centric approach provides customers with a more integrated and flexible solution. It enables them to access a wide range of tools and services through a unified interface, enhancing the overall user experience.

Comprehensive Suite of Capabilities

Phrase’s platform includes various tools for translation, quality assessment, content management, and more. This comprehensive suite ensures that businesses can manage all aspects of their language needs within a single ecosystem.

Updated Pricing Model

Along with the strategic shift, Phrase has introduced an updated pricing model that offers more value to customers.

New Pricing Structure

The new pricing model is designed to be more flexible and cost-effective. It allows customers to pay for the specific services they need, making it easier to manage budgets and optimize resources.

Benefits for Customers

This updated pricing structure ensures that customers can access Phrase’s advanced language solutions without incurring unnecessary costs. It provides better value for money and supports a wider range of businesses, from startups to large enterprises.

Strategic Partnerships with Major LSPs

Phrase’s success is also driven by its strategic partnerships with major Language Service Providers (LSPs). These collaborations enhance the company’s capabilities and extend its reach within the language technology industry.

Importance of Partnerships

Strategic partnerships are crucial for driving innovation and expanding market presence. By collaborating with leading LSPs, Phrase can leverage their expertise and resources to deliver superior language solutions.

Benefits for the Ecosystem

These partnerships benefit the entire ecosystem by fostering collaboration and knowledge sharing. They enable Phrase to stay at the forefront of technological advancements and continuously improve its offerings.

Phrase’s Ecosystem-First Approach

Phrase’s ecosystem-first approach is a key component of its strategy. This approach emphasizes the importance of building a robust and interconnected network of partners and customers.

Definition and Significance

An ecosystem-first approach focuses on creating a collaborative environment where all stakeholders can thrive. It encourages innovation, supports mutual growth, and ensures that the needs of the entire ecosystem are met.

How It Benefits the Language Technology Industry

By adopting an ecosystem-first approach, Phrase is able to drive collective progress within the language technology industry. It fosters a spirit of collaboration, accelerates innovation, and helps create more effective and comprehensive language solutions.

Conclusion

Georg Ell’s insights highlight the transformative potential of AI and generative technologies in language technology. Phrase’s innovative products and strategic initiatives are paving the way for a new era of language solutions. As the company continues to evolve, it remains committed to delivering value to its customers and partners through advanced AI-driven technologies and a platform-centric approach.