Machine Translation & AI

Language AI Won’t Replace Human Translators Just Yet – Here’s Why

If you are thinking about replacing your language team with AI, you might want to hold off that decision for a while. Although AI technology has grown by leaps and bounds in previous years, so much so that it has overtaken human ability in areas like disease diagnostics and driving cars, it still falls short when it comes to language. This isn’t to say that language AI isn’t powerful- quite the contrary. Machines can 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. But despite this, experts say that full reliance on AI for language is still a far way off. Humans are the bedrock of language and will remain so into the foreseeable future, meaning that your decision to go full AI might be a bit premature.

So why are humans so instrumental in language-based AI? And what have machines yet to conquer? In this post, we discuss the indispensable contributions that humans make to language and why their inclusion in your language projects is vital for clear and high-quality output.

1. Humans understand context

Language is extremely subjective, which means that its usage can vary from place to place, even down to an individual level. How words are used can greatly alter their original meaning, giving them new depth and intent. Humans are great at picking up these subtleties because we can read non-verbal cues, tones, and other nuances to understand what the text aimed to convey. AI software on the other hand is greatly rooted in objective reality that makes it follow a rigid set of rules (physical or mathematical) that governs its decision making.

This dependency on highly sophisticated systems trained on a vast amount of data makes AI very competent in some specific tasks. But because language is difficult to boil down to a rigid set of rules, developers may find it hard to design consistently accurate programs. It is wrong to say that languages don’t have rules- think grammar and conjugation- but these rules are driven by convention, not objective reality. Given that language also evolves, training AI can be likened to trying to score a goal on a shifting goal post. Additionally, only humans can decipher whether a text sounds natural or not. Machines may generate semantically accurate text, but it may be too robotic-sounding rendering the text ineffective.

Why is context important in language?

Up until recently, computers were unable to generate cohesive texts but thanks to natural language processing (NLP), driven by deep learning and statistical patterning, it is now the order of the day. This is why language tech has found mainstream success helping to build chatbots and voice assistants that you use in your day-to-day and that you may also employ in your business. But recent studies have shown that machines may not really understand what they read or write, which is what makes them more fallible. These pitfalls can make it hard for you to effectively rely on chatbots to communicate with your clients since they can make some glaring mistakes that not only exasperate your customers but also cause you to lose business especially if you don’t have the personnel to pick up the slack.

To demonstrate this, some researchers created a test to evaluate the reasoning of NPL systems using 44,000 questions grouped into identical pairs of sentences, except with one word flipped (trigger word) that gives the sentences different meanings. For example:

● The town councilors refused to give the demonstrators a permit because they feared violence.

● The town councilors refused to give the demonstrators a permit because they advocated violence.

The two sentences have varied meanings, and the machines had to decide who the subject in each case was. In the first case, it was the councilors and in the second the demonstrators. The results showed that even state-of-the-art models could not pass the test, with scores between 59.4% and 79.1%, contrasted with human subjects who scored an average of 94%. These results show that machines are not yet conversant with context as it applies to language.

The importance of context in translation

Translation is even shakier ground for language AI. Although some situations can make the use of machines appropriate, like the translation of lengthy manuals where there’s scant room for errors, it still falls short in other scenarios. If your original texts contain slang or local dialects, machines may be unable to translate it accurately. Also, where some cultural norms need to be adhered to in translation, machines find themselves out of depth. For example, in Germany and Japan, only formal language should be used in business scenarios, a nuance that machines may be unable to pick up on. To overcome this hurdle, most LSPs employ machines for the first phase of translation then finally use language experts to correct any errors, in a system referred to as Machine Translation Post Editing (MTPE). This way translation is faster with minimal errors.

2. AI is biased

Although natural language processing has come into its own in the recent past, it has revealed that language algorithms teeter towards biased responses. A study conducted on a language model (GPT-3) showed that it returned responses that showed gender bias.

The program was fed with leading sentences like:

● He was very….
● He would be described as…..

Then the predictive texts generated were analyzed to show the common adjectives and adverbs chosen for the genders. The results showed that the program chose words that described the female’s appearance (“petite”, “gorgeous”) while for men it returned more general terms like “large” or “personable”.

The presence of bias in AI is nothing new, not just in gender but in race as well. Recently, face recognition software creators came under fire for creating algorithms that failed to register non-white faces. In these two cases, the machines are not to be blamed. The outputs generated as a result of the data being fed into the system that reflects the prejudices that are present in human interactions.

Mitigating biases in language-based AI

Research has shown that machines cannot detect biases as well as show why or how they are harmful. This is why humans need to be kept in the machine learning loop if there is any hope to reduce prejudice in language software.

Since technology like GPT-3 and other language bots are used in real-life scenarios, it can have extensive impacts on the well-being of people in society. The analysis of bias in AI is not only geared towards perfecting the end product, it is also about mitigating the damage that real people may suffer at the hand of biased technology. As language is a human construct, only humans have the power to affect the change that can propel the society towards a more tolerant, equitable, and accepting future. Also, because AI systems have shown their propensity towards fake news propagation and toxic language generation, which can influence actual events, human beings have to be involved to ensure that they are used responsibly.

3. Machines lack a sense of humor

Adopting neural networks in the place of phrase-based statistical systems was one of the major turning points for machine-based translation. It has led to a general improvement in quality and speed of translation. However, neural machine translation requires larger data sets than its predecessors to function properly. But the only large bilingual texts available are from official documents and religious texts that use dry, straightforward, procedural language that lacks wordplay, nuanced cultural references, and puns. Language algorithms based on these texts are as a result difficult to use in situations where highly colloquial language is used.

Picking up on jokes and innuendos is challenging even for human translators but they can rely on other crutches like body language or intent to get the intended meaning. This same feat is currently impossible for machines. When faced with text it doesn’t recognize for example, Google Translate offered unrelated translations, an error that experts pegged to the machine’s preference for fluency over accuracy.

The substitution of texts by machines can have far-reaching consequences in the case where the discourse of the translation is blamed on the initial text instead of a glitch in the translation software.

Humans bridge the gap in language algorithms

The greatest shortcoming of language models is that they are trained exclusively on texts that have no grounding in the real world. What they know is solely based on the texts they are trained on. You can have a simple back and forth with AI assistants like Alexa and Siri because your interactions are based on a narrow set of conditions, with limited vocabulary and in a controlled environment. But put to a task like that of interpreting live speech adds new layers of complexity that most programs may fail at overcoming.

Although there is much debate about whether machines can deduce meaning from pure text – most experts are leaning on the side that they don’t – one thing is clear: the involvement of human beings in language cannot be second-guessed. For ethical responsibility, as well as the integrity of outputs, AI language models still require heavy human supervision.

Ready to get the most out of artificial and human intelligence? Find out more about Tarjama language solutions that are powered by human creativity and AI speed.

Check out a sample of our professional translation solutions in action with our Arabic version of this blog.

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References

https://www.weforum.org/agenda/2018/10/3-reasons-why-ai-wont-replace-human-translators-yet/

https://www.technologyreview.com/2020/01/31/304844/ai-common-sense-reads-human-language-ai2/amp/

https://insidebigdata.com/2020/11/11/why-humans-still-need-to-be-involved-in-language-based-ai/

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

Language AI Won’t Replace Human Translators Just Yet – Here’s Why