The Most Popular Language Industry Stories of 2024

As 2024 comes to a close, it is time to reflect on the most popular stories, trends, innovations, and themes that made the Slator headlines throughout the year, highlighting key developments in the language industry.

Here is a selection of stories that attracted the most attention and engagement from our readers around the world.

Will Large Language Models Edge Linguists Out of the Language Industry?

One of Slator’s most-read stories in 2024 detailed a May 2024 paper from the University of Zurich and Georgetown University that explored the role of linguists in the evolving field of machine translation (MT). The entrance of large language models (LLMs) has reduced the reliance on linguists for grammar and semantic coherence while designing a system. 

However, the authors concluded, there are a number of points in the process where linguistic expertise is still essential. These include building parallel corpora for MT; developing technology for low-resource languages; and identifying linguistic phenomena that may present challenges for a system. Linguists can be especially helpful as humans and machines interface, for example, by designing effective human evaluations and reliably assessing advancements in the field.

Google Translate Ditches Tool for Detailed Human Feedback

Google retired its longstanding human feedback tool, Contribute, which allowed users to press a button and submit an alternative translation. 

Slator reported in April 2024, Google’s announcement, in which the company acknowledged Contribute’s role in improving Google Translate, explained that since launching the tool in 2014, “our systems have significantly evolved, allowing us to phase out Contribute.” 

Users can, however, still submit feedback by rating a given translation “good” or “poor,” and, for the latter, selecting a reason from a drop-down menu — a less involved process that speakers of low-resource languages worry might halt improvement of MT for their languages. 

Live Speech-to-Speech AI Translation Goes Commercial

Just one month into 2024, an increasing number of language AI researchers — from academia to private companies — had already begun to focus on live speech-to-speech translation (S2ST). 

This only accelerated the adoption of live S2ST across multiple commercial applications thanks to LLMs, which kicked off in mid-2023, with models such as Meta’s SeamlessM4T and Google’s AudioPaLM.

Slator’s rundown of real-world use cases included business meetings, where Microsoft Translator, integrated with the Teams meeting app, provides real-time speech translation in more than 30 languages through Azure AI services. KUDO and Interprefy specialize in real-time AI speech translation for live events and conferences.

Even the high-stakes world of healthcare presents an opportunity for expansion, especially for providers already offering voice technology for healthcare clients. Orion Labs, for instance, offers live speech translation via its Push-to-Talk 2.0 platform. 

Introducing Revamped New Translation Quality ISO Standard 5060

Published in February 2024, ISO 5060 applies not only to language services providers (LSPs), but also to in-house translation departments and individual translators. While it specifically provides guidance for human evaluation of translation output, it can be used for workflows involving human and machine translation, with or without subsequent post-editing. 

The International Organization for Standardization (ISO) established a framework based on “bilingual examination of target language content against source language content,” with the goal of standardizing evaluations so they do not differ significantly from rater to rater. 

There are seven main categories of errors, which can be classified as critical, major, or minor: terminology, accuracy, linguistic conventions, style, locale conventions, audience appropriateness, and design and markup. 

Translation AI Agency Lengoo Files for Bankruptcy

In March 2024, Lengoo filed for bankruptcy in a Berlin court, with German news sources pegging Lengoo’s accumulated losses between USD 8-16m.

Christopher Kränzler, Alexander Gigga, and Philipp Koch-Büttner founded Lengoo in 2014, originally as an online platform for automating project management and administrative tasks. 

Starting in 2018, investors such as Redalpine, Creathor Ventures, Piton Capital, Inkef Capital, Techstars, and Polipo Ventures expressed confidence in Lengoo’s developing proprietary translation system, with Lengoo raising USD 34m by February 2021 — making it a long way for the LSP to fall.

Amazon Flags Risks of Training LLMs on Web-Scraped MT 

Training LLMs at scale relies on massive amounts of training data scraped from the web. A January 2024 research paper from Amazon investigating the prevalence and quality of MT on the web found that a “shocking amount of the web is machine translated” into many languages. 

And oftentimes, that MT output is low-quality, raising concerns about the quality of training data for LLMs. Researchers also noted a selection bias toward “shorter and more predictable sentences,” potentially from low-quality English content machine translated into many lower-resource languages.

The pervasiveness of low-quality MT in training data, the authors warned, could lead to less fluent models with more hallucinations, particularly for low-resource languages. 

Translators by Any Other Name

Slator’s January 2024 roundup of five polls from 2023 was crowned by the most voted-on — and perhaps introspective — question: Will the term “translator” disappear in the next five years? Close to half of respondents said no, with just over 30% saying it will “definitely” or “possibly” disappear in that time period. 

Inspired by a SlatorPod interview with ASAP-translation.com CEO Jakub Absolon, another poll asked whether readers agreed with Absolon, who suggested the term “full post-editing” should not be used, and should be priced as human translation.

More than 65% of readers agree that the term should not be used, while 18.7% want to keep using it. The remaining 16% are happy to use whatever term the client prefers. 

Other polls touched on inflation, with nearly half of respondents reporting flat rates; ChatGPT, which 80% of readers reporting they do not use it for translation; and the beloved Microsoft Language Portal, used “often” by 46.5% of respondents. 

Real-Time Speech Translation Stars in Biggest OpenAI Release Since ChatGPT

OpenAI has not slowed down since being credited with unleashing accessible AI to the masses. The company’s May 2024 release of GPT-4o offered a range of new or improved capabilities. The single new model was trained end-to-end across text, vision, and audio, with all inputs processed by the same neural network, reportedly with enhanced performance in around 50 languages. 

A demo of GPT-4o featured a brief conversation with OpenAI CTO Mira Murati asking the system a question in Italian, to which GPT-4o responds in English. Cue the hot takes of ‘RIP translators’ and shares in language learning resource Duolingo dipping 3%. OpenAI planned to launch support for GPT-4o’s new audio and video capabilities to a small group of trusted partners in an API within a few weeks.

EU Parliament Issues a New 2024 Call for Tenders for Translation Services

A February 2024 notice posted for translation services would cover translation of single and multiple source language documents in 24 languages for four European institutions: the European Parliament’s Directorate-General for Translation; the European Court of Auditors; the Committee of the Regions of the European Union; and the European Economic and Social Committee. 

While the notice did not mention MT, it did specify output metrics for source and target languages, and contracts — with one lot per language, assigned to a primary contractor and up to four secondary contractors — are estimated to last up to 60 months. No specific budget was listed. Once awarded, the contract will become effective January 1, 2025.

Bankrupt Dutch LSP, WCS Group, Quickly Bought by France’s Powerling 

In a provisional January 2024 ruling, a Dutch judge suspended payments by LSP WCS Group to its creditors, appointing an administrator to negotiate until a later hearing a few months later. Of 14 companies under the WCS Group, only one was listed as in “suspension of payment” status; all others are listed under “bankruptcy” status. At the time, WCS Group’s website listed 3,247 active freelancers, whose next steps were unclear. 

Just a few days later, French LSP Powerling acquired WCS Group for an undisclosed amount. Powerling, which already had a presence in the Netherlands — plus France, Hong Kong, and the US — said the move was in line with the company’s goal of clearing EUR 25m in revenues by the end of 2024 through acquisitions in Powerling’s main markets.

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.

Empowering Linguistic Diversity through Technology

In a rapidly globalizing world, the demand for language translation services has never been higher. However, traditional translation methods often fall short when it comes to resource-scarce languages, leaving many communities underserved and isolated. The emergence of large language models (LLMs), such as OpenAI's GPT series and Google's BERT, presents a promising solution to this longstanding challenge.

Understanding the Role of Large Language Models

Large language models are advanced artificial intelligence systems trained on vast amounts of text data, enabling them to understand and generate human-like language. Initially developed for tasks like natural language processing and text generation, LLMs have quickly found applications in translation due to their ability to grasp linguistic nuances and context.

Importance of Translation in Resource-Scarce Languages

Resource-scarce languages, often spoken by marginalized communities or indigenous groups, face numerous barriers to effective communication. Limited access to translation services exacerbates these challenges, hindering education, healthcare, and socio-economic development.

Evolution of Language Models

The field of language modeling has witnessed remarkable progress in recent years, driven by breakthroughs in deep learning and neural network architectures. Early language models like Word2Vec and GloVe laid the groundwork for more sophisticated systems capable of understanding entire sentences and paragraphs.

Applications Beyond Traditional Translation

While translation remains a primary application, LLMs have demonstrated versatility in various domains, including text summarization, sentiment analysis, and question answering. These capabilities make them invaluable tools for both researchers and businesses seeking to extract insights from vast amounts of textual data.

Challenges in Translating Resource-Scarce Languages

Despite their potential, LLMs face several challenges when tasked with translating resource-scarce languages.

Lack of Training Data

Resource-scarce languages often lack the abundant text data necessary to train robust language models. This scarcity makes it challenging for LLMs to learn the intricacies of these languages and produce accurate translations.

Preserving Linguistic Nuances and Cultural Context

Language is deeply intertwined with culture, and nuances in expression can be difficult to capture, particularly for languages with rich oral traditions or unique grammatical structures. Maintaining fidelity to the original meaning while translating into resource-scarce languages requires a nuanced understanding of both language and culture.

Can Large Language Models Bridge the Gap?

Despite these challenges, LLMs hold promise in bridging the translation gap for resource-scarce languages.

Leveraging Transfer Learning

Transfer learning, a technique where knowledge gained from one task is applied to another, has shown great success in improving the performance of LLMs on low-resource languages. By pre-training on a diverse range of languages and fine-tuning on specific language pairs, LLMs can adapt to the nuances of resource-scarce languages more effectively.

Adapting to Low-Resource Scenarios

Researchers are exploring innovative approaches to address the data scarcity issue, such as data augmentation, semi-supervised learning, and zero-shot translation. These methods aim to maximize the utility of limited training data and enhance the robustness of LLMs in translating resource-scarce languages.

Assessing the Performance

Measuring the performance of LLMs in translating resource-scarce languages requires careful consideration of various factors.

Metrics for Evaluation

Traditional metrics like BLEU and METEOR may not adequately capture the quality of translations in resource-scarce languages, which often exhibit structural and lexical differences from widely spoken languages. Researchers are developing new evaluation metrics tailored to the specific challenges of low-resource translation.

Ethical Considerations

As LLMs become more prevalent in translation, it is crucial to consider the ethical implications of their use, particularly in the context of resource-scarce languages.

Implications on Indigenous Cultures

Language is a vital aspect of cultural identity, and the preservation of indigenous languages is essential for maintaining cultural diversity and heritage. While LLMs can facilitate communication across languages, their widespread adoption should not come at the expense of marginalizing indigenous languages or eroding cultural traditions.

Bias and Fairness in Language Representation

LLMs trained on biased or incomplete datasets may perpetuate stereotypes or marginalize certain linguistic communities. Addressing bias and ensuring fairness in language representation requires proactive efforts from researchers, developers, and policymakers to promote inclusivity and diversity.

Future Directions and Opportunities

Despite the challenges and ethical considerations, the future looks promising for the role of LLMs in translating resource-scarce languages.

Collaborative Efforts in Language Preservation

Collaboration between linguists, technologists, and community stakeholders is essential for developing effective solutions tailored to the needs of resource-scarce languages. By combining expertise from diverse fields, we can leverage the full potential of LLMs to empower linguistic diversity and preserve endangered languages.

Innovations in Model Architecture and Training Strategies

Continued research and development in model architecture and training strategies hold the key to further improving the performance of LLMs in translating resource-scarce languages. Innovations such as multilingual pre-training, domain adaptation, and interactive learning offer promising avenues for future exploration.

Conclusion

In conclusion, large language models have the potential to revolutionize translation services for resource-scarce languages, opening up new opportunities for cross-cultural communication and collaboration. By addressing the challenges of data scarcity, linguistic nuance, and ethical considerations, we can harness the power of LLMs to preserve linguistic diversity and promote cultural understanding on a global scale.

Language AI Briefing May 2024

Language AI, or Artificial Intelligence designed to comprehend, generate, and interact in human languages, continues to evolve at a rapid pace. The May 2024 briefing highlights significant advancements in this field, ushering in a new era of communication and innovation.

Advancements in Language AI

In recent years, Language AI has witnessed remarkable progress, driven by breakthroughs in deep learning algorithms and access to vast amounts of linguistic data. These advancements have propelled the development of AI models capable of understanding, generating, and translating human languages with unprecedented accuracy and fluency.

One of the most notable advancements is the refinement of Natural Language Understanding (NLU) models, enabling machines to comprehend human language in context, grasp nuances, and respond appropriately. This development has profound implications for various applications, including virtual assistants, customer service automation, and content creation.

Moreover, Language AI has made significant strides in enhancing multilingual capabilities. AI models can now seamlessly translate between languages, breaking down communication barriers and facilitating global collaboration and exchange of ideas.

Key Highlights from the May 2024 Briefing

The May 2024 briefing showcases several groundbreaking achievements in Language AI:

Breakthroughs in Natural Language Understanding

Researchers have achieved unprecedented levels of accuracy in NLU tasks, such as sentiment analysis, semantic parsing, and question answering. These advancements pave the way for more intuitive human-machine interactions and personalized user experiences.

Enhanced Multilingual Capabilities

Language AI models have been trained on diverse linguistic datasets, enabling them to understand and generate content in multiple languages with remarkable proficiency. This development opens up new possibilities for cross-cultural communication and localization efforts.

Integration with Emerging Technologies

Language AI is increasingly being integrated with other emerging technologies, such as augmented reality, virtual reality, and the Internet of Things (IoT). This convergence leads to innovative applications, such as immersive language learning experiences, AI-powered virtual assistants in smart homes, and real-time language translation in augmented reality environments.

Implications for Various Industries

The advancements in Language AI have far-reaching implications across various industries:

Healthcare

Language AI-powered virtual assistants and chatbots can streamline patient communication, provide medical information, and assist healthcare professionals in diagnosis and treatment planning.

Finance

AI-driven language analysis tools can analyze financial reports, detect fraudulent activities, and provide personalized financial advice to clients, enhancing efficiency and accuracy in financial decision-making.

Education

Language AI platforms can revolutionize language learning by offering personalized tutoring, interactive exercises, and real-time feedback, making language acquisition more engaging and effective for learners of all ages.

Entertainment

Language AI technologies are transforming the entertainment industry by enabling personalized content recommendations, automated content creation, and immersive storytelling experiences, catering to diverse audience preferences and interests.

Challenges and Future Directions

Despite the remarkable progress in Language AI, several challenges remain to be addressed:

Ethical Considerations

As Language AI becomes more pervasive in our daily lives, ethical considerations regarding privacy, bias, and algorithmic fairness become increasingly critical. It is essential to develop robust ethical guidelines and regulatory frameworks to ensure responsible and equitable use of AI technologies.

Addressing Bias

AI models are susceptible to bias inherent in the datasets they are trained on, leading to biased outcomes and discriminatory practices. Addressing bias in Language AI requires ongoing efforts to diversify datasets, mitigate algorithmic biases, and promote transparency and accountability in AI development and deployment.

Future Prospects

Looking ahead, the future of Language AI holds immense promise, with potential applications spanning education, healthcare, business, and beyond. Continued research and innovation in areas such as multimodal learning, lifelong learning, and human-AI collaboration will further advance the capabilities of Language AI and unlock new opportunities for societal impact and economic growth.

Conclusion

The Language AI Briefing May 2024 highlights the remarkable progress and transformative potential of Language AI. With advancements in Natural Language Understanding, enhanced multilingual capabilities, and integration with emerging technologies, Language AI is poised to revolutionize communication, collaboration, and innovation across industries. However, addressing ethical challenges and biases remains imperative to ensure the responsible and equitable deployment of AI technologies.