Featured, Technology, Translation

Machine Translation: What is it and How Does it Work?

Gone are the days when translating one language to another required the use of a bilingual dictionary. Nowadays, when you come across words that are in a foreign language, you log on to an online translation platform and get the results instantly. This is basically what machine translation is; an automated process of rendering one language to another. The use of machine translation has become so common that Google Translate reports that it translates over 100 billion words a day.

Aside from personal use, machine translation (MT) helps brands and businesses expand their reach to global audiences. Now more than ever, website content is being translated into numerous languages to help break the language barrier. By doing so, they are not only able to expand to new international markets but are also helping to demarginalize groups that were initially not privy to information on the internet.

Machine translation has an edge over human translation due to its speed and cost. With computers, the translation is instantaneous, at less than a third of the cost. Despite the improvement of the output from MT, the general perception by professionals and businesses that it cannot be a substitute for human translation is still prevalent. Some companies opt to integrate their translation process; using MT for initial translation then doing some editorial work to further improve the quality and accuracy. Used correctly, machine translation can expedite the translation process without compromising on the quality of the output content.

When Should I Use Machine Translation?

Where large volumes of content need to be translated. Translating whole websites or large documents especially when time is a critical factor, solely depending on human translation is often not feasible. Machines can run uninterrupted for long periods, delivering results almost instantly. Human translators can then edit and review the machine translations to ensure that the final content is polished and accurate.
Where nuances are unimportant. Language in nature often has ambiguous rules; therefore limiting words to specific styles can be difficult. However, the language employed in manuals or software documentation is often straightforward, making the use of MT appropriate. Machine translators work by translating singular words or sentences in parallel. The result is a collection of translated sentences in sequence, not a cohesively translated text. Problems with flow, fluency, and readability are often inherent to machine-translated texts. This is especially true for technical or creative work where the usage of words is mostly contextual.
Where the translation budget is limited. Companies are always on the lookout for techniques that will reduce their expenditure. This is why many small scale firms turn to machine translation for their work- machines cost less than human labor. To lower costs and maintain the integrity of the translated text, some businesses prefer to use a mix of both machine and human translators.
Where the content is ephemeral. Content subject to change such as customer reviews, emails, or FAQs are generated quickly and has only one-off usage. Their quality doesn’t need to be as high as that of professional documents. The quality of the translation is also not important when it is to be used for in-house research.

How does Machine Translation Works?

Machine translation works by using software to convert one language (source language) to another (target language). Although it sounds straightforward, complex processes go into making even the most basic of translations possible. Different types of MT systems are in use today:

1. Rules-Based Machine Translation

This was the first commercial translation system to be used. RBMT is based on the premise that languages have grammatical, syntactical, and semantic rules that govern them. These rules are predefined by human experts in both the source and target languages and rely heavily on a robust bilingual dictionary. The translation takes place in three phases: analysis, transfer, and generation.

Building RBTM is often time-consuming and expensive but has higher quality outputs compared to others. The vocabulary used can be updated or edited easily to refine the quality of the translated text. These refinements can help the texts read more fluently and remove the machine-like quality that some tend to have.

RBMT works best when translating between languages whose rules are dynamic and abstract.

2. Statistical-Based Machine Translation

SBT relies on the use of statistics to generate translations based on parameters that are derived from the analysis of existing bilingual sets of texts, known as text corpus. Unlike Rules-Based Machine Translation which is word-based, SBT makes uses of phrases that reduce the rigidity imposed on the algorithm by word to word translation.

The statistical models that are derived from the extensive analysis of the bilingual corpus (original and target languages) and the monolingual corpus (target language) work to define which words or phrases are more likely to be used.

The large volume of texts required to run SBT systems has become more available due to the extensive use of the internet and cloud computing. Although the translated output of SBT has higher fluency compared to RBTM, the statistical-based translated text is less consistent.

3. Neural Machine Translation

Neural Machine Translation is the advanced version of SBT. It makes use of a large artificial neural network that predicts the likely sequence of long phrases and sentences. Unlike statistical-based translation, NMT uses less memory since the models are trained jointly to maximize the quality of the translations.

Neural networks (like the ones in our brains) make use of encoder/ decoder technology. During the learning phase, these networks automatically correct the set parameters by comparing the output to the expected translation and then make the necessary adjustments. This means that they require to be trained by humans in order to work. It involves feeding the program with large volumes of data; a process that takes only a few weeks.

NMT is the most advanced method of machine translation and makes use of complex algorithms such as deep learning and AI. This enables it to learn new languages and can be relied upon to produce consistently high-quality output. NMT is currently in use on successful translation platform, Google Translate.

Advantages of Neural Machine Translation

Fewer errors compared to other systems. NMT-generated translations require less post-editing work. In fact, the translated texts have 50% fewer grammatical and 17% fewer lexicon errors compared to other MT systems. For this reason, NMT is often used to produce professional translations.
Uses less data. Typically, NMT calculates the probability of the use of a word or phrase through existing bilingual texts. However, for languages with low resources, it draws parallels from its existing lexicon and uses it to build a translation system, eliminating the need to input new data.
Enables direct zero-shot translation. When translating between two languages, for example, French and Portuguese, other translation systems will first convert the text to English, then to the target language. This causes significant delays in the translation process. With neural machine translators, however, translations are made directly from the source to the target languages, even where no language translation engine between the two exists. This is powered by deep learning technology.

Disadvantages of Neural Machine Translation

Clarity is needed in the source text. Just like with other machine translation systems, the original text needs to be clear and coherent to prevent meaningless translations.
It still requires human input. A significant amount of man-hours must go into training the program before it can work efficiently. In line with the issues of context, human verification of the translated document is important to ensure that the text makes sense. Proofread machine translation or post-edited machine translation as it commonly referred to, is still a long way from becoming obsolete. Not until machines learn to pick up on the subtleties of language will we fully rely on them to produce non-erroneous text.
Data privacy infringement. NMT works by learning and constantly improving its database. In doing so, any translated data must be stored for future use.

Evaluation of Machine Translation

The quality of the translated content is the most important aspect of translation. This is why linguists and programmers have been trying to create tools that can rate the quality of translations ever since the inception of machine translation in the 50s.

Two approaches can be used while evaluating translations:

Glass box evaluation– measures the quality of the translation based on the internal mechanisms of the translation system.
Black box evaluation– based solely on the quality of the output. This is the more commonplace paradigm used by translators today.

The evaluation is based on a predetermined set. The set comprises of the sentences in the source language and their partner texts in the target language. Translated texts are then compared against these sets, and if they are of the same style, a match will be detected.

Types of MT Evaluation

1. Manual Evaluation

Humans read through the final text to check its accuracy. The main pointers during the evaluation are fluency and adherence to the meaning of the source text.

When checking for fluency, the source text is unimportant. The evaluator reads through the translation to ensure that it is free of grammatical or syntactical errors.

Then, the text is compared to the original to ensure that it has not veered too far from the message of the source material.

2. Automatic Evaluation

The score obtained is based on the premise that the output should be as close to human translation as possible. Therefore, automatic evaluation relies heavily on pre-existing translations. To improve the accuracy of the score, the evaluation process should be repeated often due to the dynamic nature of languages and MT systems.

Metrics used in the evaluation include METEOR, NIST, and BLEU. They compare the translated text to the reference material and often work without the need for human interference.

Final Thoughts

Machine translations have improved significantly over the years and are helping many firms localize their content to reach a global audience. Properly used, they can produce high-level output with minimal human input. Although they are far from being used independently, MTs are useful for large volumes of content where human translation may be impossible.

Looking for a reliable and secure Machine Translation for your confidential business material? Book a demo now to see Tarjama’s proprietary MT that enables you to deliver multilingual content quickly, securely, and accurately – geared to incorporate your industry’s jargon and writing style into all your translations.

References

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

Machine Translation: What is it and How Does it Work?