Machine Translation & AI

What is a Translation Management System and Do I Need One

The pace of content production in the world has increased more than ever, and it is expected to grow even more with developing technologies. The content produced appears in many different text formats. The age of the internet and technology has brought with it a massive amount of content that’s increasing day by day.

Today’s translation industry is busy translating this content into one language or more simultaneously and into different formats, such as user manuals, websites, software interfaces, quickly and with high quality. Thus, speed and quality provide a significant competitive advantage to both translators and employers. Many translation software and technology serve to produce the anticipated quality in the most efficient way possible.

One of those irreplaceable translation technologies is translation management systems (TMS). Here you can discover what a translation management system is and find out if you need one.

What is a Translation Management System?

To summarize, a translation management system (TMS) is a centralized platform that simplifies the management and automation of translation. A TMS automates repetitive tasks involved in translating content, significantly reducing manual operations and improving control and efficiency of the entire process. This reflects in increased consistency, speed, and productivity while lowering costs.

Translation memory systems store translations of sentences, words, phrases, or numbers in source-target pairs in databases. These pairs are called translation units (TUs). In the translation process, the source text segment to be translated is compared with the database of translations made before – that is, the translation memory – and the target text segment. At this pre-translation stage, the translator can also see the matching percentage. This helps translators work on each segment only once, saving companies indispensable time and money, in addition to producing consistent translations across all projects and formats

Translation memory is often integrated with workflow management, machine translation, project management, terminology management, and translation editor tools. This operational system is called a TMS, and this term is widely used by the translation industry.

Translation management systems manage the continuous translation flow, including localization, sharing linguistic data, and automating workflows based on project and terminological information held in the database, thereby implementing reusable translation processes. Every step in the translation process can be tracked by TMS users, regardless of whether it is using internal or external sources.

TMS combines project management capabilities with workflows, connects to translation memory and translator tools, and stores information about individual translation resources.

How Does It Work?

The translation management system allows the sorting and classification of translation memories. This ensures maximum reuse of previously translated texts. Translation memories can be accessed in many ways, providing flexibility in offline and online translation resources, ensuring the highest benefit from translation memories. In addition, translation management solutions integrate seamlessly with multiple databases for terminology management. For example, within the online edit and review functionality, terms are automatically highlighted so that the translator and editor can always see and reuse previously translated terms.

A task-based workflow consisting of the project team and a system is created for each content type and target language. Outsourced translators can receive notifications via email when they have work to do, and users can also see a web-based summary of all their pending tasks. Projects can be created based on any number of localization jobs, allowing consolidated planning of each phase of the project and setting specific milestones, with reminders sent to users before deadlines. In addition, the project manager has full visibility on the status, budget, and results of each job and resource.

Who Can Benefit From TMS?

Both project managers and translators benefit from a TMS. Translation is a process that needs to be kept under constant control, so a translation management system is a vital necessity for project managers. With the help of a TMS, the project manager does the planning within the team, ensures the management of the project resources and the team, and has full visibility on monitoring, reporting, and optimization. So, project managers in the translation industry should use translation programs with management features to keep all these factors under control.

A TMS significantly reduces translators’ workload. With programs focused only on translation and taking into account the translator’s needs, the process accelerates, and errors are minimized. In addition, there are benefits such as the ability to quickly correct mistakes, easy archiving, and access to the archive when working on a document. Consequently, process management becomes easier; the translator automatically knows how many words to be translated and how many hours the translation will take.

The fact that all these processes are faster and error-free satisfies both translators and customers.

What are the Perks of a Translation Management System?

Since the amount of translation is constantly increasing and translators cannot fully meet the demand, machines and automation have to be included in the translation process. Translation management systems make your translation process faster, more automated, simpler, and saves you time. With a TMS, repetitive and troublesome manual work reduces, control over the process enhances, and productivity boosts.

Translation memories make it easier for translators at this point. The essential feature of a term bank is storing previously translated texts, words, and phrases in a database and then using them to translate similar texts.

Translation management technology accelerates the translation process, consolidates and centralizes linguistic values, and enables collaboration across the entire translation supply chain.

– Automatic processes: By automating manual processes, translation costs and time decrease.

– Linguistic consistency: Centralized translation memory, terminology, and dictionaries ensure consistency and accuracy across the project.

– Collaboration: Increased collaboration and integration between translators, departments, and service providers.

Features and Additional Benefits

Term Bank and Translation Memory

Translation memories in TMSs are indispensable to translators.

Another advantage of translation memory systems is that a term bank can be integrated into many of these memories. In this way, translators can see the translation memory suggestions for sentences and phrases on their screens. At the same time, they can also see the dictionary equivalents of words in a panel of the translation memory system window. Thus, parallel to the capacity of the translation memory used, the time to find the matches gets shorter.

The most outstanding benefit of term banks is that it enables more efficient use of time, which is vital for translators. It is also crucial for translators to ensure the consistency of the term equivalents used in translations.

There are several ways to create a term bank, which is a tool that can be used by both an independent translator or a company with dozens of translators. Translators can create a term bank in its simplest form by entering the new terms they encounter while translating into a database.

Machine Translation

Some TMSs include an integrated machine translation (MT) tool that allows for instant translations. Often machine translation output is used to translate bulks of content at an initial stage, after which human translators can review and polish it. This process, namely Machine Translation Post-Editing (MTPE) helps save time for translators, who can just work on reviewing and editing the translators and focus more on the creative elements.

Backup and Archiving

With a TMS, all your data is backed up on a secure server. The team does not have to deal with the storage. Critical information such as uploaded files, translation memories, terminologies, translator profiles, and customer information is regarded with secure backups. A TMS keeps your files securely stored and encrypted, and provides you with the flexibility to choose who has access to which files.

Project Management

Work or project management includes planning, organizing, coordinating, controlling, auditing.

– Knowing which translator and editor are more suitable for the task,

– Knowing the translation status,

– Knowing the points that the customer objected to in the text, or at least understanding why the team translated the text with a specific approach,

– Deciding whether the customer is right in objection, and if so, how the editor or the translator can achieve the correct translation are regular responsibilities of project managers.

Collaboration

Project managers customize their workflows to fit their business needs. Each participant in the workflow receives a notification that a new job is to be done and can be followed. Translators and editors, working both online and offline, and their queries and comments are tracked through the system. Translators or editors receive comments from the client to validate and implement any corrections.

How to Choose The Right TMS?

A TMS brings together all the tools that companies use to manage their translations in one platform. Therefore, it is the foundation for all other technologies, which is why the selection and purchase of a TMS require careful consideration in advance. Your existing technology, the dynamics and diversity of your content and languages, and, in particular, your budget should be taken into account.

There is almost no limit to the factors that should be considered before deciding to invest in a TMS. You should always pay attention to a few basic functionalities, such as an easy-to-use editor interface and intelligent search functions. For some businesses, however, the translation software they use will depend on more specific requirements.

For example, think about the size and geographical spread of a company. The TMS manager needs to know how many people will use the translation programs, whether the TMS needs multilingual functions, and how extensive the support required is for the team to keep the project going smoothly.

It is also important to consider the level of control both managers and end-users will have in using the TMS. Businesses also need to consider the variety of electronic data forms they use. The best translation software should easily index all types of digital content.

Here are some additional guidelines for choosing the right TMS:

– Get recommendations from employees and managers.

– Understand who will be using the TMS and for which purposes they will use it.

– Include translators, editors, project managers, the marketing team, and IT staff in the decision-making process.

– Assess current and future business needs.

– Consider whether the system can be integrated into the company’s current tech stack and whether the TMS has optimization functions.

– Research your options and make a shortlist of important software that meets your needs.

– Contact TMS providers. This gives them an opportunity to address the needs of your business and explain why they can offer the right software for translators.

– Evaluate the functionality, training, and support that each provider gives. With the help of demos, you can understand how the particular system works and which integrations it offers.

We, Tarjama, use a highly advanced TMS called CleverSo, integrating translation technologies that minimize your translation costs and maximize our, and your, team efficiency. Combining AI with human perception, tasks become easier and production boosts up. Discover our TMS technology and get immersed in the new era of translation.

AMT in 2024: Leading the Way with Cutting-Edge Features for Seamless Translations

In today’s fast-paced world, businesses face the ongoing challenge of managing multilingual content across various platforms and file types. As companies expand globally, the need for efficient, accurate, and scalable translation solutions has become critical. AMT (Arabic Machine Translation) has risen to meet this demand with groundbreaking features introduced in 2024, designed to solve common translation challenges and simplify workflows. Here is a closer look at how AMT’s new features tackle these challenges and make translation more accessible than ever: 1. Web Extraction: Simplifying Website Translation The Challenge: Translating websites is often tedious and requires accuracy and maintaining a site’s layout and structure. Ensuring consistency across languages for e-commerce platforms or corporate pages can be time-consuming. The Solution: AMT’s Web Extraction feature allows businesses to translate entire websites or specific web pages seamlessly. By extracting and translating content while preserving the formatting, AMT ensures a flawless user experience for multilingual websites. 2. PDF Support: From Upload to Translation in DOC Format The Challenge: PDF documents are one of the most complex formats to translate due to their rigid structure. Many translation engines struggle to maintain accuracy, leading to inefficiencies in post-translation editing. The Solution: AMT now supports PDF upload, with the option to download the translated text in an editable DOC format. This simplifies the translation of contracts, presentations, and reports, providing an accurate translation while ensuring ease of use. 3. Multi-Format File Upload and Download: Flexibility for Diverse Business Needs The Challenge: Modern businesses use various file formats, from spreadsheets to PowerPoint presentations. Translating across these different file types without losing formatting or structure is a major challenge. The Solution: AMT’s multi-format support allows businesses to upload and translate documents in formats such as DOCX, PPTX, XLSX, and HTML. After translation, users can download their content in the same format, ensuring the structure and data remain intact. Why These Features Matter: With these powerful new features—Web Extraction, PDF Support, and multi-format upload/download—AMT simplifies the translation process, ensuring that your content is accurate and ready to use in any language. Experience these features Now Register at translate.tarjama.com and transform how your business documents and multilingual content translation.

September 11, 2024

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