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    The Role of Language Service Providers in the Age of AI

    Introduction

     

    Since the rise of ChatGPT-4 in 2023, AI has significantly impacted various industries, including translation.  Many organizations are curious about how AI affects the translation business and what the future holds for language service providers (LSPs).  Let’s explore this topic from our unique perspective as an LSP, delving into the challenges and opportunities that AI presents in the world of translation.

     

    To start, we need to define AI in this context as “AI after 2023” because AI prior to this time was mainly machine translation that carried negative connotations for many years.  Today AI in translation encompasses machine translation (MT), large language models (LLM), and generative AI.  While AI has made great strides, it’s not always simple for organizations to adopt these technologies for their own translations at scale.  This is where LSPs can add value by embracing and customizing these advancements to meet specific client needs.

     

    The Challenge in Translation

     

    Here is how AI has tackled translations up to this point.

     

    Machine Translation (MT) relies on large volumes of bilingual text to determine the most probable translation between languages.  This approach tends to produce accurate but sometimes literal translations.  MT engines are trained on specific language pairs and domains, making them highly effective for certain types of content.

     

    Large Language Models (LLMs), on the other hand, are trained on vast amounts of monolingual text data and generate translations by predicting the next word in a sequence.  While LLMs were not expressly created for translation, this method can yield fluent but sometimes inaccurate translations, with a risk of hallucinations or made-up information.

     

    The challenge for translation, whether by human or otherwise, is balancing accuracy and natural language.  It’s difficult to produce a translation that is both fully accurate and entirely fluent.  Usually what is fully accurate can “read like a translation”, and what is entirely fluent requires deviating from the source language.

     

    With AI technology, this challenge still exists in that we either have models that look at accuracy first (MT), or fluency first (LLM).  This is where the expertise of human translators and editors becomes crucial, especially for content that requires nuance, cultural sensitivity, or technical expertise.

     

    Can AI replace language service providers?

     

    While AI has advanced significantly, LSPs will still be needed, albeit with an evolving role that increasingly leverages AI for their clients.  Here are two scenarios that illustrate why organizations may find it challenging to adopt AI for translation on their own:

     

    1. Branding Guidelines and Linguistic Assets

    Organizations often have specific branding guidelines, glossaries, and style guides that define how terms should be translated across languages.  They may also use Translation Memories (TMs) for consistency and efficiency.  Managing these linguistic assets effectively requires constant oversight from project managers, language technologists, and linguists, as well as investment in a robust translation management system (TMS).

     

    LSPs play a crucial role in maintaining and updating linguistic assets, ensuring that all translations adhere to the client’s brand voice and terminology preferences.  This level of customization and attention to detail is difficult to achieve with off-the-shelf AI translation tools.

     

    2. Complex Translation Jobs

    Many translation projects involve multiple languages, timelines, special file formats, and additional services such as technical writing, typesetting, and voice recording.  Each of these cases need multi-layer workflows that are not easily automated by AI alone and require human expertise to coordinate and execute effectively.

     

    Addressing complex workflows is the primary focus at DataSource, which has enabled us to grow with numerous clients over the years.  For example in 2023, we translated an average of 4.8 languages per order.  Also, most of our jobs were for technical documentation such as instruction manuals, and they usually require any combination of technical writing, translation, typesetting and product photoshooting.

     

    The Future of Translation: Opportunities for LSPs

     

    While AI has made simple translation tasks more accessible to the general public, complex jobs still require expertise that goes beyond what ChatGPT or Google Translate can offer.  This presents several opportunities for LSPs:

     

    1. Proprietary AI models

    Larger LSPs can offer services at the LLM or MT engine level, competing with established AI translation tools.  By developing proprietary AI models or customizing existing ones, LSPs can provide unique value to clients with specific industry or domain requirements.

     

    2. Translation Solutions for Complex Workflows

    Flexible LSPs, like DataSource, can help clients address complex translation workflows through the nimble use of various language technologies in combination with competent human linguists.  Smaller LSPs are usually quicker when custom solutions are needed.

     

    3. Continuous Innovation

    The rapid rate of evolution of AI technology will also make it difficult for the casual adopter to keep pace.  There will be many new language models and versions to study and apply.  LSPs that successfully navigate this landscape by consistently evaluating, adopting, and integrating cutting-edge AI models and software into their processes will continue to differentiate themselves in the eyes of their clients.

     

    4. Specialized Services

    LSPs can focus on niche markets or specialized content types that require a high level of expertise and cannot be adequately served by general-purpose AI translation tools.  This could include legal, medical, or technical translations that demand subject matter expertise and strict accuracy.

     

    Conclusion

     

    The integration of AI in the translation industry presents both challenges and opportunities for Language Service Providers.  While AI is revolutionizing the way we approach translation, the human touch remains crucial, particularly for complex and sensitive jobs.  LSPs that master AI technology and combine it with expert human linguists will play a vital role in managing the increasing demands of the global translation market.  By embracing AI as a tool to enhance their services rather than viewing it as a threat, LSPs can position themselves as indispensable partners in the evolving landscape of global communication.