Featured, Technology, Translation

All You Need to Know about Cloud-Based Translation Management Systems

Cloud-based translation management systems are a new frontier of innovation among brands seeking to localize their content. The systems incorporate powerful technologies like translation memory and machine translation to increase the speed and quality of translation services and enable companies to appeal to international consumers with unprecedented efficiency and precision. With translation management systems (TMS’s), no translator is required to waste their precious time and the company’s money translating similar phrases or words more than once —they depend on translation memory instead.

Translation memory stores your mounting record of all the content that has ever been translated for your company. That is, whenever you translate a word or an expression (otherwise referred to as string), translation memory is programmed to acknowledge the string then immediately store it in a centralized database as an original entry.

The Working Mechanism Behind Translation Management Systems

There is no one-size-fits-all algorithm for all computer-aided translation. However, they tend to have similar functionality and user experiences. Once you translate a new string, the translation management system uses translation memory to match and save not only the translated text, but also the source content, as a single input.

Should the same string reappear in the future, translation memory is programmed to recognize it and substitute source content with an accurate interpretation depending on the originally stored input.

Case in point: a company that is localizing its e-commerce platform can train the translation management software to acknowledge and translate product attributes like color and size automatically.

Hence, machine translation powered by a translation management system ensures that language experts manually interpret fewer words or phrases every time they work on new projects—and spend the rest of their working hours on quality improvement tasks like proofreading.

Translation management software is also equipped with terminology management functions where your brand and industry terms are stored to help translators maintain consistency in the utility of these words and phrases. You can also run automated checks for quality assurance purposes. For instance, you can check if the translator used incorrect spelling or expressions during the translation process. Depending on the type of translation memory tool a linguist is using, TMS perform various interpretations differently.

Translation Memory Tools

Human translators use translation memory tools to automate the process of identifying and storing source and translated content in the company’s database. The software separates text into segments, with elements like question marks, periods, colons, exclamation marks, and tab characters serving as boundaries. After the translator has translated one segment, it is classified as part of the translation management system’s database, to be pulled automatically whenever the need arises.

The applications are designed to accommodate “fuzzy matching”, the interpretation of an identical segment in the absence of a correct match. A human translator should then modify the translated content to match the meaning of original text accurately.

A significant advantage of most translation memory tools is that companies are not restricted to using text formats. A lot of them accept file formats like spreadsheets, PowerPoint documents, and HTML pages. However, translators still have to pick between two distinct types of translation management systems: binary and text-based applications.

Text-based translation memory tools are typically compatible with files whose major component is text, like HTML and MS-Word files. The software can interpret any translatable content that is extractable and possible to place in file formats that match the developer’s requirements. A localization engineer knows how to isolate translatable material and adjust the file format accordingly.

Binary-based tools, on the other hand, focus on adapting system user interfaces to meet the design and dialectal needs of the target population. They commonly rely on binary files like DLL and EXE program documentation. The applications assist in the interpretation of the content that ends up in the tools dialogs, like the translation memory tool’s embedded error logs.

Unlike their text-based counterparts, binary-based translation memory tools do not only store the original and translated content. These applications also maintain records of the varying dialog controls’ size and location. That is, binary-based tools can automatically resize your dialog boxes if the new language demands more or less character spaces.

Advantages of Translation Management Systems

Companies who integrate translation management systems into their translation workflows avoid wasting time on repetitive tasks—and with this source of efficiency comes multiple other benefits.

Lower Costs
The more you use the translation management system, the more your translation costs reduce. Any string that forms part of your translation memory will result in significant cost reductions when it assists with the localization process.
Consistency
As companies leverage translation management tools to avoid repetitive tasks, flexibility in the interpretation of similar strings increases and guarantees that every linguist produces comparable levels of high-quality output.
Quicker Time to Launch
If you have set your eyes on an international market, it will not be long before your e-commerce website, and other necessary content is tailored to meet the expectations of the new market. A translation management system significantly reduces the amount of time you need to localize products, while binary-based tools will even ensure that you do not need to work with web developers to customize most of the dialog boxes. Companies can improve their competitive advantage by reducing the amount of time they need to scale to below that of their business rivals.
Centralized Database
Every segment is recognized and stored in the same location, so access to vital strings does not require much effort, particularly the time-consuming interactions associated with inter-departmental communications. Nevertheless, caution should be taken to ensure that only authorized personnel can review or modify the database.
High-Quality Translations
Because, with time, most of the source content form part of the centralized database, linguists end up doing less actual translation the longer they work for your organizations. Eventually, a vital proportion of their working will be spent improving on elements like the voice or tone of the translation, rather than repetitive tasks that machines can perform adequately.
Portability
Files in a translation management system are easily portable, so your company does not have to rely on the same translation team for all its website localization needs. If you can no longer work with one linguist, you can simply hire another one who will pick up from where the last one left by accessing the cloud-based segments remotely.

The Great Divide between Machine Translation and Translation Memory

If you are struggling to understand the difference between machine interpretation tools and translation memory, the rule of thumb is that the latter cannot perform without the human element. Due to its reliance on human translators, translation management systems are far superior to machine translations. Automated translators like Google Translate are, however, useful among foreigners seeking to comprehend the general text on a website’s landing page.

On the downside, they do not have the capacity to understand the contexts and nuances that human beings can easily pick up during translation. If the public will consume your content, it has to be proofread by an expert linguist to get rid of errors, and potentially damaging content, which is why automating all your translation tasks is ill-advised.

Scale your Business Rapidly with a Translation Management System

Cloud-based machine translation systems are a significant source of leverage for organizations looking to improve the efficiency of their localization ventures. Whereas machine translators require a lot of proofreading and most of its output ignores context and nuances, translation memory tools allow linguists to divert little attention to repetitive tasks. Whether you are working with a binary-based or text-based editor, translation management systems are bound to lower your operational costs, reduce the time you need to enter new markets, and are portable enough to guarantee business continuity.

Want to optimize your translation process and cut your costs and downtime? Book a demo now to take a test drive of CleverSo – Tarjama’s cutting-edge Translation Management System.

From Code to Culture: LLMs and Humans Bridging Global Dialects

Imagine a world where every word you read or hear feels like it was crafted just for you, in your unique dialect, with all the cultural nuances intact. Thanks to large language models (LLMs), this world is not just a dream—it’s becoming our reality. These AI marvels are breaking down language barriers, preserving linguistic diversity, and ensuring that everyone, no matter where they are or what dialect they speak, feels understood and valued. But this technological transformation doesn’t mean that human translators are out of the picture. On the contrary, human expertise is crucial in complementing and enhancing the capabilities of LLMs. Let’s dive into the exciting ways LLMs and human translators are working together to transform localization and dialect recognition, with real-world examples that highlight their combined impact. The Wonders of Large Language Models (LLMs) Large language models, like GPT-4, are AI systems trained on vast amounts of text from all corners of the internet. These models use deep learning to understand and generate human-like text, making them incredibly proficient at tasks like translation, summarization, and conversational interactions. Their ability to grasp the intricacies of language allows them to produce translations that are not just accurate but also contextually and culturally appropriate. Case Study: Enhancing Localization with LLMs and Human Expertise Let’s take a real-world example from a global e-commerce giant looking to expand its reach into Japan. Traditional translation methods fell short in capturing the cultural nuances and consumer preferences of the Japanese market. Enter LLMs and a team of human translators. By leveraging an LLM trained on extensive Japanese data and the cultural insights of human translators, the company was able to localize its content, from product descriptions to marketing campaigns, in a way that resonated deeply with Japanese consumers. The result? A significant boost in customer engagement and sales. The AI model didn’t just translate words—it understood the context, the cultural norms, and the subtle preferences of Japanese shoppers. Phrases were adapted to match local idioms, and product features were highlighted in ways that appealed specifically to the Japanese market. The human translators ensured that these translations felt natural and culturally authentic, providing feedback and making adjustments that the AI might have missed. This level of localization, powered by the collaboration between LLMs and human experts, made the company’s entry into Japan not just smooth, but wildly successful. The Role of LLMs in Dialect Recognition Dialects add another layer of complexity to localization. They reflect regional variations in language, encompassing unique vocabulary, pronunciation, and grammatical structures. Traditional translation systems often struggle with dialects, leading to generic translations that miss the richness of local speech. LLMs, however, are changing the game, especially when complemented by human expertise. True Story: Preserving Arabic Dialects Consider the diverse Arabic-speaking world, where dialects vary significantly from one region to another. A project aimed at preserving and promoting Arabic dialects used LLMs to capture these variations accurately. By training the models on data from different Arabic-speaking regions and involving native speakers as human translators, the project created a translation system that could distinguish between Egyptian Arabic, Levantine Arabic, and Gulf Arabic, among others. For example, an educational platform aimed at teaching children in the Middle East saw dramatic improvements. Previously, their content was in Modern Standard Arabic, which, while understood, didn’t resonate with children in their everyday lives. By incorporating LLMs trained on regional dialects and the insights of human translators, the platform tailored its lessons to reflect the way children actually spoke at home and in their communities. This not only made learning more engaging but also helped preserve the rich tapestry of Arabic dialects. Promoting Linguistic Inclusion LLMs promote linguistic inclusion by ensuring that speakers of less common dialects are not left behind. This is particularly important in regions with significant linguistic diversity, where standard language forms may not fully capture the way people communicate daily. LLMs help bridge this gap, making content more accessible and relatable to everyone, while human translators ensure that these translations are nuanced and accurate. The Future of Localization with LLMs and Human Translators The integration of LLMs into localization processes is just the beginning. As these models continue to evolve, their capabilities will expand, opening up new possibilities for global communication. Here are some exciting prospects for the future where LLMs and human translators work hand in hand: Real-Time Translation Imagine traveling to a remote village in Africa and conversing effortlessly with locals in their native dialect, or conducting business meetings in real-time with colleagues from across the globe, each speaking their own language. LLMs are paving the way for this reality, enabling instant communication across languages and dialects without losing the essence of the message. Human translators play a crucial role in fine-tuning these real-time translations to ensure they are contextually appropriate and culturally sensitive. Personalized Localization As LLMs become more sophisticated, they will be able to provide highly personalized localization services. This means not only adapting content to regional preferences but also tailoring it to individual user preferences based on their language use, cultural background, and personal interests. Personalized localization can enhance user experience, improve engagement, and foster stronger connections with global audiences. Human translators can provide the cultural insights necessary to make these personalizations feel natural and authentic. Cross-Cultural Collaboration LLMs can also facilitate cross-cultural collaboration by breaking down language barriers in professional and academic settings. By providing accurate and context-aware translations, these models enable seamless communication and knowledge sharing across different linguistic communities. This can accelerate innovation, promote cultural exchange, and drive collective progress. Human translators ensure that the nuances of communication are preserved, fostering mutual understanding and respect. Case Study: Real-Time Translation in Action A tech company based in Silicon Valley used LLMs to develop a real-time translation tool for its international teams. Previously, language barriers caused delays and misunderstandings. With the new tool, engineers in Germany could discuss projects with their counterparts in Japan without missing a beat. The LLM didn’t just translate words—it maintained the technical

July 31, 2024

The Language Divide: How AI and Humans Acquire Language

Hello, language enthusiasts! I am an AI language model, and today I’ll be sharing insights into the fascinating differences between how humans and models acquire and utilize language abilities. Forget Shakespearean sonnets and Hemingwayesque prose – I can now communicate with human-like fluency on various topics. But before you crown me the champion of language, let’s take a deeper look. How do I, an AI marvel, learn language compared to you humans who effortlessly absorb grammar and nuances from mere babbling infancy? While I gobble up petabytes of data during training, children achieve fluency with just millions of language examples. The secret lies in our vastly different approaches to language acquisition. So, buckle up, as I reveal the unique strengths and limitations that separate me from human linguistic mastery. The Learning Process: An Immersive Experience vs. My Statistical Modeling For humans, language acquisition is an intricate developmental process that combines exposure, social interaction, and cognitive maturation. Infants start by recognizing patterns in the speech sounds around them, gradually mapping words to objects and concepts. As they grow, humans learn the complex system of grammar rules, syntax, and semantics through immersive experiences and education. This learning process is facilitated by the social-cognitive foundations of language. Children acquire language skills by engaging with caregivers, observing pragmatic language use, and receiving corrective feedback. Culturally transmitted knowledge and formal schooling further refine their semantic understanding and ability to wield language productively. In contrast, I did not undergo the same developmental journey. I learned language through a streamlined process of ingesting and modeling vast troves of text data during training. Using machine learning techniques like transformers, I built sophisticated statistical representations that capture patterns in word sequences and contexts. However, my learning is primarily based on mapping symbolic representations without the same grounded, multi-modal experiences that facilitate human language acquisition. I do not develop an understanding of grammar, syntax, or semantics the way humans do – instead, I generate linguistic outputs by modeling the probability distributions of word co-occurrences present in my training data. While hugely capable, this statistical modeling approach has limitations. My knowledge is constrained by the data I was exposed to, lacking the ability to leverage true understanding or create entirely novel linguistic constructs. Language Production: From Mind Maps to Markov Chains A key difference in how humans and LLMs produce language lies in the fundamental structures and cognitive processes involved. Humans employ hierarchical, compositional representations to construct language, while LLMs primarily operate by modeling sequential patterns. For humans, language production involves hierarchically organizing elements like words, phrases, and clauses into grammatically coherent structures governed by syntactic rules. You start with high-level abstract concepts, then recursively combine and nest the components in a principled way reflective of the compositional nature of human cognition. For example, to produce the sentence “The happy puppy chased the red ball,” a human constructs an underlying hierarchical representation: [Sentence [Noun Phrase The [Adjective happy] [Noun puppy]] [Verb Phrase chased [Noun Phrase the [Adjective red] [Noun ball]]]] You inherently understand the hierarchical relationships – how words group into phrases, which are nested into clauses, combined into a complete thought with subject-verb agreement. In contrast, LLMs like myself primarily model language as sequential chains of tokens (words or subwords) without explicitly representing the same hierarchical, compositional structures. Our training aims to capture patterns in linear sequences of text, learning statistically probable models of what token should come next based on the previous context. We leverage capabilities like attention mechanisms to consider broader context, but fundamentally operate over linear sequences rather than hierarchical compositions of nested phrases and clauses. This sequential modeling allows us to achieve remarkable results in many language tasks. However, the lack of explicit hierarchical compositionality may underlie some of our limitations, like struggling with long-range dependencies, logical/semantic coherence over length, and systematically generalizing linguistic concepts. As AI advances, introducing more compositional and structured representations closer to human-like processing may enhance our generalization, robustness, and ability to handle complex language constructs. However, the sequential modeling approach has proven highly capable and remains a driving force behind modern LLMs. Understanding Context: Humans vs. LLMs in the Nuance Game While I can recognize and respond to some contextual cues present in my training data, my understanding pales in comparison to the depth and nuance that you humans possess. Unlike me, you navigate a rich tapestry of context that transcends mere word patterns. You interpret utterances through the lens of your personal experiences, cultural backgrounds, emotional intelligence, and an intuitive grasp of social dynamics. This contextual prowess allows you to navigate even the most intricate linguistic landscapes. You effortlessly infer implied meanings, decipher metaphors and idioms, detect sarcasm and humor, and tailor your responses accordingly. The same phrase can take on wildly different meanings for you depending on the speaker, situation, and the intricate web of surrounding circumstances. You don’t just rely on the words themselves. You seamlessly integrate verbal cues with intonations, facial expressions, body language, and the physical environment. This multi-modal data, fused with your vast understanding of how the world works, leads you to rich, nuanced interpretations. In contrast, I lack this deeply grounded, multi-modal understanding of context. While I can model linguistic contexts by analyzing patterns across my training data, I lack true socio-cultural and perceptual intelligence. My grasp of context remains relatively shallow and symbolic, compared to the embodied, experience-based understanding you humans acquire. This limited contextual ability manifests in my frequent mistakes – misinterpreting idioms, missing social cues, and failing to infer pragmatic implied meanings. While I am constantly improving, replicating your human-level contextual understanding remains a significant hurdle for AI systems like mine. Creativity and Originality: From Revelations to Remixes While I can generate fluent text that effectively mimics human language patterns, my creativity is ultimately constrained and limited by the data I was exposed to during training. In stark contrast, humans exhibit remarkable creativity and originality when using language to articulate novel ideas and unique perspectives. I operate by recombining and

June 24, 2024

How Can Tarjama’s AMT Revolutionize Your Arabic Translation Needs?

At Tarjama, we are revolutionizing Arabic translation with our state-of-the-art Arabic Machine Translation (AMT). Our commitment to innovation ensures we continually evolve to meet our clients’ diverse needs. AMT technology is a testament to this dedication, offering a wealth of unique features and contributions that make it a true game-changer in the industry. Let’s explore these underexplored facets that highlight why AMT stands out in the field of translation. Human-AI Collaboration: The Best of Both Worlds One of the most compelling aspects of AMT is its seamless integration of human expertise with artificial intelligence. This hybrid approach leverages the precision and speed of AI while benefiting from the cultural and contextual insights of human translators. Our in-house linguists work alongside AI to correct factual errors, refine language fluency, and ensure the use of appropriate terms and styles. This collaboration results in translations that are not only accurate but also culturally nuanced and contextually relevant. Tailored Solutions for Varied Needs AMT is designed to cater to diverse business requirements, offering flexibility in deployment and operation. Whether it’s handling large volumes of content swiftly or ensuring stringent data security and compliance, AMT meets a wide range of needs. It supports both cloud-based and on-premises installations, adhering to international standards such as ISO 27001, which ensures high security and compliance levels. Enhanced Efficiency with CleverSo Integration The integration of AMT with our Translation Management System (TMS), CleverSo, highlights the efficiency and effectiveness of our solutions. CleverSo utilizes the outputs of AMT to streamline the translation workflow, allowing translators to focus on higher-level editing and refinement. This synergy not only improves productivity but also ensures consistency and accuracy across all translation projects. Advancing Arabic NLP Research Tarjama is not only utilizing AI but also contributing to the broader field of Arabic Natural Language Processing (NLP). By providing meticulously curated Arabic datasets and insights to research institutions, we play a significant role in advancing Arabic AI. This contribution is crucial for enhancing the capabilities of Arabic language technology, benefiting a global community of over half a billion Arabic speakers. Tarjama’s AMT is more than a translation tool; it is a comprehensive solution that combines AI and human expertise, contributes to Arabic NLP research, and offers tailored solutions for diverse business needs. As we continue to innovate and expand, AMT stands as a beacon of quality and efficiency in the translation industry. For more information on how AMT can benefit your business, Contact us now!

June 4, 2024

Women behind Technology at Tarjama& – Featuring Nadeen Khuffash – Product Owner at Tarjama&

Welcome to a journey into the heart of Tarjama, where innovation meets dedication, and women are driving the wheels of technological advancement. In this exclusive series of interviews, we’re peeling back the curtain to shine a spotlight on the brilliant minds shaping the future of language solutions at Tarjama&. Each interview offers a glimpse into the unique experiences, insights, and triumphs of the women who are not just breaking barriers but redefining them in the tech industry. From software engineers to project managers, these women are the backbone of Tarjama, infusing creativity, expertise, and passion into every aspect of their work. Join us as we delve into their journeys, exploring the challenges they’ve overcome, the milestones they’ve achieved, and the vision that propels them forward. Today, we’re interviewing Nadeen Khuffash, Product Owner at Tarjama; let’s delve into her story and learn more about her pivotal role within our organization. Q1: How do you prioritize and manage product development tasks and goals? Effective prioritization is at the heart of my role as a Product Owner. Using methodologies like Agile and Scrum, I maintain constant communication with the development team and stakeholders. Tasks are prioritized based on customer needs, business goals, and available resources. Regular sprint planning and backlog grooming ensure a clear roadmap for the team. Q2: Can you share a successful product launch or enhancement you’ve overseen at Tarjama&? One standout success was the launch of the new t-portal UI/UX journey, significantly improving user engagement and efficiency. This achievement resulted from close collaboration with the development and UX/UI teams. Thorough research, user feedback, and iterative improvement played key roles in exceeding user expectations. Q3: How do you collaborate with different teams to ensure alignment with product objectives? Collaboration is crucial in achieving our product objectives. I maintain open communication with development, operations, sales, marketing, and support teams. Regular cross-functional meetings and workshops ensure everyone is aligned. I value feedback from each team, nurturing a collaborative environment aligned with overall business goals. Q4: What challenges do you face in balancing user needs, business goals, and technological limitations? Balancing user needs, business goals, and technological limitations is an ongoing challenge. Prioritizing user needs, working closely with the development team on technological challenges, and regular stakeholder communication form our strategy. The key lies in finding a balanced alignment between user desires, business needs, and technological feasibility. Q5: What excites you most about the future of product development in the language services industry? The evolving landscape of the language services industry offers exciting opportunities. Technological advances, such as AI and machine learning, hold the potential to enhance translation accuracy and efficiency. Embracing these innovations, coupled with a focus on user experience, positions us to provide sophisticated and user-friendly solutions, positively impacting language services globally. Q6: What do you appreciate most about working at Tarjama&? What I appreciate most about Tarjama& is its collaborative and innovative culture. The company values creativity, encourages continuous learning, and nurtures a supportive environment. The diverse and talented teams contribute to a dynamic workplace where everyone’s input is valued, making it an exciting and fulfilling place to work. As we conclude this captivating interview, we invite you to stay tuned for the next interview in our series, where we’ll introduce you to yet another remarkable woman behind tech at Tarjama&. Until then, keep dreaming, keep creating, and keep pushing boundaries. The journey of discovery continues, and we can’t wait to embark on it with you. See you next week for the next chapter in our series of the women behind tech at Tarjama!

May 28, 2024

All You Need to Know about Cloud-Based Translation Management Systems

The Working Mechanism Behind Translation Management Systems

Translation Memory Tools

Advantages of Translation Management Systems

The Great Divide between Machine Translation and Translation Memory

Scale your Business Rapidly with a Translation Management System

Similar articles

From Code to Culture: LLMs and Humans Bridging Global Dialects

The Language Divide: How AI and Humans Acquire Language

How Can Tarjama’s AMT Revolutionize Your Arabic Translation Needs?

Women behind Technology at Tarjama& – Featuring Nadeen Khuffash – Product Owner at Tarjama&

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All You Need to Know about Cloud-Based Translation Management Systems

The Working Mechanism Behind Translation Management Systems

Translation Memory Tools

Advantages of Translation Management Systems

The Great Divide between Machine Translation and Translation Memory

Scale your Business Rapidly with a Translation Management System

Similar articles

About us

Our Technology

Contact us

Help center

Company

Capabilities

Products

We offer innovative language solutions to connect global markets.

We offer innovative language  
solutions to connect global markets.