Machine Translation & AI

Machine Translation vs Human Translation

Machine translation has undoubtedly taken center stage in the translation industry in recent years – with its ability to translate large documents instantaneously, carry on simple conversation, produce written texts and do a myriad of other things that make our day-to-day lives easier. Consequently, it has brought important questions to the table which are being asked by companies, and linguists alike: Can machine translation (MT) surpass human translation? Should there be a comparison? How did translation technologies become vital contributors to human translators for perfect outcomes? Or, what are the responsibilities of human translators in the MT process?

In this post, we’ll deep-dive into these questions and discuss the comparison of machine translation vs human translation as well as the dynamics and relationship between the two. Keep reading to find out!

The Invention of Machine Translation

Machine translation (MT) research began in 1954 with the Georgetown Experiment in partnership with Georgetown University and IBM. The successful results of the Georgetown Experiment using six grammar rules and 250 words in its memory led to an increase in the studies on machine translation. Some of the methods used in machine translation are Dictionary-Based MT, Rule-Based MT, Example-Based MT, Statistical MT, and Neural Machine Translation. You can find out more in our blog article about machine translation and how it works.

Machine vs. Human

Although the use of computers in translation has a recent history, it has developed quickly and continues to grow due to rapid technological advances. For example, computerized translation included only word translation through dictionary programs at first; but over time, it has improved with the translation of long texts formed by highly complex sentences. Today, translations are done automatically because, starting from simple words, compound sentence structures, and even complex sentence structures are stored in databases.

However, machine translation is unlikely to replace humans. Localization does not mean transferring a word from one language to another; it is the process of transferring content from one language to another while keeping the terms of meaning and style. So, it is not a simple process. It consists of understanding the source text, analyzing its linguistic structure, and expressing the thoughts and feelings obtained from the source language into the target language. Therefore, localization is a multistage process rather than simply translating the source language in the target language.

For a machine to perform such a process should have a substructure to analyze the language structure. However, the versatility of languages constantly creates expansions and changes. Thus, translation requires a more complex method than previously thought. Nevertheless, the role of machine translation systems in providing solutions is undeniable. This is because it has become faster and more precise.

Especially MTPE, Machine Translation Post-Editing, is a breakthrough process in the translation industry as it decreases the workload of translators. The machine does the heavy translation in bulk and the human translator does the editing and reviewing, with more time to focus on the creative touch that only humans can provide.

Significant Advances on Machine Translation

Machine translation, which initially meant translation done by machine only, gave way to computer-assisted translation (CAT) as the necessity of human intervention was revealed over time. In computer-assisted translation, the human translator makes corrections, additions, and edits. With the use of technology in translation, features that evolved from the base of MT offer various usefulness to language service providers.

In the 1970s, translation memories were developed in CAT software that stores the work for the translator’s following translations. In translation memories, which were initially based on matching at the sentence level, more successful matchings have emerged with the studies on the variables of sentences over time. In addition, since every change and improvement made by the translator to the translation is saved in the translation memory, the need to retranslate similar sentences decreases.

When translation memory is used in conjunction with machine translation, you’re able to save time, costs, while delivering consistent, accurate, and high-quality translations.

Uses and Functions of MT

Machine translation has a place in all sectors of the translation industry. Surely, machine translation uses differ depending on the type of translation process and the text to be translated. The diversity of content and purposes affect the functions of MT.

The term covers two overarching processes: raw machine translation and Machine Translation Post-Editing (MTPE). Raw MT is where the human translator does not interfere, while MTPE combines both machine and human translation.

One of the determining factors in this regard is the type of document. For example, raw MT is inadequate for texts such as literary writings where the author’s style needs to be reflected. Because it is difficult for the machine to determine what the author means precisely and translate accordingly. On the other hand, raw MT is helpful for texts that do not require interpretation and intervention from the translator, such as internal documents, emails, chatbot conversations, and non-customer-facing content.

Pros and Cons of MT

Using MT technology in translation has various benefits, such as:

– Increasing translation speed,

– Doing more translations at lower costs,

– Minimizing translation errors caused by human translators.

In this context, MT and the human translator undertake specific but significant tasks and functions in translation practices. However, the translator has always been at the center of the translation process. The translator, acting as a bridge between two languages, is the essential factor that connects the languages. Technology has shown its presence in the translation profession and has become even more critical.

It is possible to talk about the positive and negative effects and results of technology in every part of the translation sector.

– Translators have become anxious, fearing that MT might replace them. The machine, which had a minimal translation capability at first, is increasingly able to translate very complex sentences.

– There is a decrease in translation fees paid to translators. Since the translation process is done partially by machine and faster than a human, employers pay the translators less, thinking MT does the entire work.

However, technological developments in translation also have positive outcomes for translators, such as:

– Localization and transcreation, practices that are crucial for marketing purposes for example, has become such an important process that we can say that translators must know how to use machine translation systems, contributing to their professional development.

– MT provides spare time for the translators so that they can focus more on self-development. The support MT provides lets translators be more creative and have the time to advance in languages.

The Roles of Human Translators in MT

We can analyze the roles of translators under four main subjects: producer, controller, contributor, and post-editor.

Translator as Producer

Although the advances in machine translation have reached a high level today, the success demanded of all projects is at the maximum. Translators undoubtedly undertake an essential part of this work. Because ‘translation’ and ‘technology’ are terms that express two different industries, combining them actually paves the way for experts in these two different fields to come together and form a team. It checks the accuracy of the translations that the system produces. Since machine translation involves at least two different disciplines, conducting studies on the subject by a team of LSP allows more potent results. Therefore, the translator has an essential role in developing machine translation software.

Translator as Controller

MT systems that allow translator’s intervention have integrated features like translation memories and term banks. The translator undertakes the most critical roles, translating and editing, and machine translation systems provide tools that the translator can benefit from.

While using these tools, the translator also acts as a controller. For example, when a translator is doing a translation with computer-assisted translation software, they have the opportunity to check whether the translation memory is functioning correctly. As a result, the translator has constant control of whether the translations that have been done before are saved into the translation memory. If so, to what extent will it assist in the following translation work. Thus, the translator fulfills a vital function in machine translation by being a controller of the whole process.

Translator as Contributor

The development of machine translation systems continues through studies done on production and control data. The translator can correct the deficiencies by evaluating the information acquired from controlling the system. In addition, the translator has a contributing role during the translation of some content where machine translation does not achieve the expected outcome. A translator who works with different text types, especially literary translations where conveying the author’s style is crucial, can communicate with the author, the editor, or the publisher and clarify the problematic areas. In this way, the human translator makes significant contributions to developing the software, translation memory, and term banks of MT systems.

Translator as Post-Editor

Another role that the translator undertakes is being a post-editor. The final corrections of the machine translation are crucial. This process requires a very different approach than a simple spell-check. The translator’s post-edition smoothes the translation out and gets rid of the mechanical tone of MT. The translator evaluates the process from beginning to end and makes the necessary additions, omissions, explanations, and corrections.

Machines cannot interpret. However, the translator can, acting as an intermediary and transmitter between the two languages, cultures. The task done by the machine is a translation, and human controls and transfers it; transference is multidimensional and genuine, whereas translation is mechanical.

The Key is to Combine – a.k.a Augmented Intelligence

Augmented intelligence uses both artificial and human intelligence to get the best out of both worlds. There have been many discussions covering machine translation vs. human translation. However, it is better to combine them rather than compare them. Human translators turn this technology into a device and use it as an aiding component in translation. The human translator will always take a role, different duties, and responsibilities in translation. For this reason, technological developments in translation will continue to be gateways to major expansions.

Discover now how you can optimize the cost, speed, and quality of your translation projects by using both MT and expert linguists hand-in-hand. Contact us for more information.

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