Category: MT & Postedition

Voice-Over Revolution: The Rise of Dynamic Dubbing - Portada
17/06/2025

Voice-Over Revolution: The Rise of Dynamic Dubbing

Posted on | by Litzaqueline Briceño | Leave a Comment on Voice-Over Revolution: The Rise of Dynamic Dubbing

AI has found its voice—literally. In the world of media localization, AI-powered voiceover and dubbing tools are transforming the way that organizations create multilingual content. These technologies offer a faster, more scalable approach to audio localization, making voiceover capabilities accessible to a wider range of industries and budgets. While this shift is promising, it also demands that localization experts strike an increasingly thoughtful balance between automation and human expertise. 

Behind the Scenes: How AI Dubbing Works 

At the heart of AI dubbing lies advanced speech synthesis, a technology that turns adapted scripts into lifelike voiceovers. Here’s how it works: 

  1. Input: A script or text file is provided to the AI system. This script has been previously adapted to the required language by a professional media localizer, and it’s properly adjusted to the time constraints of the video. 
  1. Processing: The AI system treats the script or text file with a technology called speech synthesis—also known as text-to-speech or speech-to-speech. This engine has two main components: a front-end and a back-end. The front-end prepares the text by converting symbols, like numbers and abbreviations, into full words. This process is often referred to as text normalization. The front-end also determines how each word should sound (aka text-to-phoneme conversion) and organizes the text into phrases or sentences with appropriate rhythm and intonation (prosody). 
  1. Output: The back-end of the engine, also called the synthesizer, takes this prepared information and converts it to sound: the final speech output. This product can range from a neutral voiceover to region-specific accents, depending on the tool’s capabilities. 

The advanced speech synthesis workflow is especially useful for scalable, lower-budget projects like e-learning, product tutorials, corporate training, or even social media content. For creators and companies alike, it brings a powerful opportunity to localize at scale—without the complexity of a traditional studio setup. 

Table comparing different content types by readiness, characteristics, emotion level, and main goal. It includes six categories: educational content, audio description, low-emotive entertainment (documentaries), content creators, marketing, and high-emotive entertainment (video games, movies, shows).

When Is AI Dubbing the Right Fit? 

The table below offers a more-detailed breakdown of the ways in which AI dubbing can be a smart choice for certain types of content, particularly where production budgets are limited or speed is crucial. Use cases range from educational content and audio descriptions to lower-emotion entertainment, like documentaries. Content creators and marketers are also turning to AI dubbing to expand their reach—especially with platforms like YouTube integrating autodub features. 

But as the table also indicates, there can be limits to AI’s efficacy with content consumers. For emotionally charged productions like films, TV shows, or video games, for example, audiences expect nuance. This is where AI still struggles—and where human-generated post-editing must bridge the gap. 

Enter the Scene: The Role of Post-editing 

Post-editing plays a pivotal role in refining AI-generated dubbing to ensure it resonates with audiences and meets production standards. Beyond technical edits, post-editing also involves prooflistening—a final review that focuses on the listener’s experience. This step helps detect subtle inconsistencies or audio distractions that may go unnoticed in the editing process. 

To deliver high-quality results, post-editors must bear in mind: 

  • Regional Accents and Voice Consistency, which ensure cultural authenticity and coherence throughout the audio, and are especially important when dubbing for specific markets or across multiple episodes or segments. 
  • Speech Rate and Pronunciation, which improve clarity and flow by adjusting unnatural pacing or correcting mispronunciations. These are the sort of fixes that help the audience stay effortlessly engaged. 
  • Emotional Accuracy, which ensures the tone reflects the original performance. Whether the source content was exciting or languid, comical or sincere, the dubbed version must feel genuine and fully aligned with the original material’s intent. 
  • Lip-Sync Matching. This component of post-editing oversight, which is essential for visual content, ensures that spoken words align with on-screen lip movements. It serves to safeguard audience immersion and maintain professionalism in video formats. 
  • Client-Specific Audio Specs. These considerations cover everything from volume levels to file formatting. They ensure that the audio is ready for final delivery and integration with other production elements. 

Each of these post-editing considerations contributes to a seamless viewer experience—and it’s the human attention to these very human details that sets the final product apart. 

Conclusion 

AI dubbing is evolving fast, bringing greater access to localized voiceover than ever before. For global organizations and creators with growing content demands, it’s a powerful tool that unlocks speed and scale. But even as the technology improves, human involvement in the dubbing process remains essential. That’s specially true when the goal isn’t just about being understood, but about being truly heard—in the right voice, at the right time, by the right audience. 

Ensuring Quality in MTPE The Role of ISO 18587 - Portada
13/05/2025

Ensuring Quality in MTPE: The Role of ISO 18587

Posted on | by Kemel Cobresle | Leave a Comment on Ensuring Quality in MTPE: The Role of ISO 18587

As global content demands continue to grow, Machine Translation (MT) has become a complementary tool in the language industry. Its speed and cost efficiency make it an attractive solution for companies looking to scale their multilingual communication. However, while MT technology has come a long way, it isn’t without its limitations—especially when it comes to nuance, cultural sensitivity, and industry-specific language.  

This is where human expertise enters the equation through Post-Editing (PE), creating a powerful synergy known as Machine Translation Post-Editing (MTPE). Let’s dive into why ISO 18587 plays such an important role in MTPE. 

The Role of ISO Standards in MTPE 

Post-editing enhances MT output by addressing the gaps that machines alone can’t close. By refining grammar, improving style, and ensuring the message resonates with local audiences, PE ensures that translations are not only technically correct but also culturally and contextually accurate. The result is content that sounds natural, is consistent in terminology, and meets professional standards—something that’s increasingly essential in today’s competitive global marketplace. 

To ensure consistency and quality in MTPE, industry standards play a vital role. One key benchmark is ISO 18587, a standard specifically developed for the post-editing of machine-translated content. Rather than focusing on strict compliance, ISO 18587 provides a framework for what high-quality post-editing should look like. It defines the competencies required of post-editors, outlines best practices for workflows, and sets clear expectations for quality and accuracy. For companies, adopting processes aligned with this standard helps streamline multilingual content creation while maintaining high standards of clarity and reliability. 

Why Post-Editing is Essential for Quality 

Why is post-editing necessary, even with increasingly sophisticated MT engines? Despite advances in AI, raw MT output often struggles with: 

  • Linguistic accuracy: Errors in grammar, syntax, and sentence structure. 
  • Cultural and contextual relevance: Lack of adaptation to local customs, tone, or idiomatic expressions. 
  • Style and readability: Robotic or awkward phrasing that diminishes the content’s impact. 

Post-editors address these shortcomings, bringing human judgment and linguistic nuance into the process. Their role is not just corrective—it’s transformative, shaping MT output into polished, publication-ready content. 

Best Practices for High-Quality Post-Editing 

Achieving high-quality MTPE also means applying best practices across projects. This includes: 

  • Full post-editing vs. light post-editing: Choosing the appropriate depth of editing based on content use. 
  • Linguistic and cultural adaptation: Refining messages to align with audience expectations and local context. 
  • Quality assurance (QA) measures: Incorporating review cycles, style guides, and reference materials to ensure professional standards are met. 

The Takeaway 

In short, MT is a powerful asset, but its full potential is realized only when paired with expert human post-editing. For companies looking to produce accurate, localized, and brand-consistent content, investing in professional MTPE is essential. As an ISO 18587-certified company, at Terra, we understand the value of combining technology with linguistic expertise. Partnering with a language service provider that follows ISO-based processes guarantees high-quality outcomes for diverse global audiences. 

An Insider’s Guide to Solutions Architects Balancing Innovation and Ethics
01/04/2025

The Role of Solutions Architects in Ethical Innovation

Posted on | by Litzaqueline Briceño | Leave a Comment on The Role of Solutions Architects in Ethical Innovation

The localization industry has long relied on traditional methodologies to deliver first-rate translation work. But as technology continues to reshape the ways in which businesses communicate across borders, many translators are taking enthusiastic advantage of new and evolving tools to streamline their workflows and increase efficiency.  

To employ innovations like artificial intelligence (AI) and machine translation (MT) effectively and responsibly, localization experts must seamlessly integrate them into their processes while still ensuring that human expertise is driving the quality of the project.  

Few in the industry are better equipped to take on this challenge than a solutions architect (SA). Among their many responsibilities, which we outlined in an earlier post, solutions architects remain ever-mindful that any technology they use to augment any localization project has to complement human expertise—and not replace it.  

Let’s explore more about how an SA works to strike this essential balance between innovation and ethical localization. 

Ensuring Technology’s Positive Impact on Localization

An Insider’s Guide to Solutions Architects Balancing Innovation and Ethics - Sara Rodríguez

Localization technology has the enormous potential to transform workflows on a broad scale. Solutions like AI-powered translation systems can reduce turnaround times and ingest large volumes of content, while automation can simplify repetitive tasks. Such upsides are undeniable, but even so, these tools can present challenges. For example, human overreliance on technology during the localization process runs the risk of losing cultural nuance, and lack of human oversight can lead to biased or inaccurate translations.  

Solutions architects circumvent these pitfalls by crafting workflows that incorporate technological innovation to enhance human proficiency. “The key to using AI and MT is balance,” explains Sara Rodríguez, a solutions architect at Terra. “We integrate these tools into workflows to expedite processes, but we always require human oversight to maintain the highest level of quality control.” 

For instance, MT has the capability to generate initial drafts of translations for high-volume content—of, say, a technical manual. A solutions architect will ensure that the MT was carefully and securely trained on industry-specific glossaries and quality standards so that the technology’s output is as cohesive and informed as possible. However, the SA will also ensure that linguists then join the workflow to further refine the MT’s draft for even stronger accuracy and cultural relevance.  

Similarly, a solutions architect might design a workflow in which AI accelerates various repetitive tasks, which frees professionals to focus on facets of localization that require creativity and cultural expertise. 

The Vital Role of Human Oversight

In the end, it is that very human creativity which is so essential to preserve, and which remains an irreplaceable factor in successful localization. Technology alone cannot deliver it. Human translators bring cultural sensitivity, context, and an understanding of idiomatic expressions to their work in ways that AI cannot replicate.  

At Terra, solutions architects bear this in mind as they ensure that human input and checkpoints are prioritized and utilized in every workflow. In addition, our SAs remain vigilant for any project-related ethical considerations that must be made for people who may be affected by the technologies in use. 

“We think a lot about the EU’s General Data Protection Regulation,” says Sara. “The right of end users to not be subjected to a decision based solely on automated processing should be enforced.” 

Conclusion

As technology continues to shape the localization industry, the role of SAs becomes more critical than ever. By leveraging AI and MT responsibly while safeguarding cultural integrity, solutions architects build trust and deliver meaningful, authentic global communication. Their efforts not only ensure successful localization, but also reinforce the vital human connection that lies at the heart of every project. 

An Insider’s Guide to Solutions Architects: The Key to Long-Term Client Success
19/03/2025

Insights from Solutions Architects to Achieve Long-Term Client Success

Posted on | by Litzaqueline Briceño | Leave a Comment on Insights from Solutions Architects to Achieve Long-Term Client Success
Photo of Marina Ilari, CEO of Terra.

Localization projects are intricate in that they require a blend of linguistic expertise, technical know-how, and strategic vision. At the heart of any successful localization effort lies a pivotal role: the solutions architect (SA). An SA’s involvement goes far beyond initial setups, as Terra CEO Marina Ilari explains. “SAs play a very important role in building and maintaining long-term client relationships. They act as proactive, trusted partners who are committed our clients’ success.”  

Let’s explore the multifaceted responsibilities of solutions architects and their impact on long-term partnerships.  

Building a Foundation of Trust 

Photo of Sara Rodríguez, a solutions architect at Terra.

Throughout the localization process, trust between a client and its translation team is the cornerstone of a successful partnership. Projects often involve sensitive data and high-stakes outcomes, making it essential for both parties to be willing to build strong relationships from the start. SAs are instrumental in fostering this trust by prioritizing open communication and recognizing each client’s unique needs. 

“Genuine relationships with clients rest on trust and proactive conversations,” says Sara Rodríguez, a solutions architect at Terra. “An SA ensures that clients feel supported from the first day by taking the time to understand their concerns, aspirations, and preferences.” This personalized approach lays the groundwork for collaboration and ensures that clients feel confident throughout the process.  

Tailored Solutions for Every Challenge 

Localization isn’t a one-size-fits-all process. Every client has distinct goals and challenges, and it’s the job of a solutions architect to design workflows that address these nuances. From Translation Management Systems (TMS) to automation tools, SAs leverage a range of technologies to create scalable solutions. 

“We develop strategies to address bottlenecks in workflows, for example, or we’ll integrate technology like CAT tools,” says Sara. “I’ll often be working to streamline processes to meet a client’s business goals like market expansion or regulatory compliance,” she adds. 

By tailoring workflows to meet specific requirements, SAs ensure that translation projects achieve their immediate objectives, while also remaining flexible for any adjustments clients need to make in the future. For instance, by implementing secure TMS systems with role-based access and real-time quality checks, Terra’s SAs have helped clients manage high-volume, time-sensitive projects with confidence in the moment, while ensuring those same systems and checks will remain workable for clients down the line.  

Supporting Growth Through Continuous Optimization 

Delivering results is only the beginning of a solutions architect’s work. As they stay actively involved throughout the lifetime of a project, they provide support and refine processes along the way. This iterative approach not only boosts operational efficiency but also ensures adaptability in industries like gaming or healthcare, where localization needs can evolve rapidly. 

Soledad Coronel, one of our solutions architects, explains, “The goal is to apply industry best practices in design and development, which include design patterns, scalable architecture, and quality standards.” An SA’s commitment keeps workflows robust and adaptable so clients continue to meet their objectives. 

One notable example of effective continuous optimization that we executed at Terra involved a high-volume video game task that required localization into 15 languages under tight deadlines. The SA in charge developed a workflow that featured contextualization tools and automated checks throughout the life of the project, which resulted in our delivering high-quality translations on time.    

Language, Technology, and the Irreplaceable Human Touch 

In today’s localization landscape, technology is indispensable. But even with advances in AI and automation, the human element remains essential to the localization process. SAs know firsthand the subtleties of what human translators bring to the table, and in an increasingly tech-driven world, SAs reinforce the importance of the human touch by leveraging it in the right places. 

Photo of Soledad Coronel, one of our solutions architects at Terra.

“Translators and linguists bring cultural nuances and idiomatic accuracy that machines can’t replicate,” says Soledad. “Our role requires us to understand that gap between human linguistic expertise and cutting-edge technical solutions, so we can effectively implement the strengths of both.”  

From automating repetitive tasks to managing the human element of complex workflows, SAs bring clarity and optimal efficiency to every project, all while staying true to the client’s vision.  

“Our work really demands a unique blend of cultural sensitivity, technical expertise, and strategic thinking,” Soledad adds. “And then, beyond understanding those tools, it’s about aligning those them with client objectives to deliver impactful results.” 

Conclusion 

The role of a solutions architect is pivotal to achieving successful and long-term client relationships in localization. By combining technical knowledge, cultural insight, and a client-first approach, SAs help businesses navigate the complexities of global markets with confidence.   

Their work not only addresses immediate project needs, but also lays the foundation for sustainable growth and continued success. For any organization aiming to excel in localization, partnering with a skilled solutions architect is a step toward achieving impactful and lasting results.

AI Ethics Essentials Key Principles for Responsible Implementation
04/03/2025

AI Ethics Essentials: Key Principles for Responsible Implementation

Posted on | by Litzaqueline Briceño | Leave a Comment on AI Ethics Essentials: Key Principles for Responsible Implementation

As artificial intelligence (AI) tools become a vital part of modern business, it’s easy to view them as a silver bullet for company-wide efficiency, cost savings, and innovation. But, as the saying goes, with great power comes great responsibility. No matter how a company employs AI, using it ethically should not be a choice, but an essential practice to build trust, ensure fairness, and protect the people who use these tools or are impacted by them. 

Following sound ethical principles for AI, such as these laid out by the European Union, can make these sorts of positive differences right from the start. From there, organizations should take the time to periodically reevaluate their AI tools to ensure they continue to align with their values and safeguard their users. Here, we’ll cover seven fundamental concepts that every company should consider before implementing AI solutions. 

1. Transparency in The Usage of AI Solutions 

Transparency is the foundation of ethical AI. When team members or clients interact with AI solutions, they should be able to easily follow how the tool reaches its conclusions or outputs. It is recommended that all processes involving the use of AI be identifiable and documented for future analyses and management. 

2. Support for Human Agency 

AI should empower, not replace, human decision-makers. Keeping human power at the fore ensures that these tools act as partners to human expertise, rather than substitutes. AI-driven systems can offer recommendations and insights, but as the European Union’s AI guidelines note, “The right of end users not to be subject to a decision based solely on automated processing should be enforced.” Following this approach ensures that technology enhances, rather than diminishes, the human touch on the job at hand. 

3. Reliability and Safety 

Reliability and safety are essential pillars of ethical AI. Before implementation, it is crucial to verify that AI works as intended and performs consistently over time. Reliable usage of AI technology is especially important in areas with high-stakes outcomes like healthcare or pharmaceuticals, in which even small errors can have significant consequences. 

4. Diversity, Non-discrimination & Fairness 

An ethical approach to AI usage respects diversity and prioritizes inclusion, which means it must be tested carefully for unintended biases and further evaluated to ensure it serves a wide range of users equitably. While AI systems aren’t inherently designed with inclusivity in mind, using them responsibly can support diverse populations and reinforce a company’s commitment to fairness. The goal should be to create or leverage tools that acknowledge and adapt to the unique backgrounds of all users.  

5. Privacy Protection and Data Security 

Data security is paramount in ethical AI use, as the technology relies on vast amounts of information. For clients, prioritizing their privacy means ensuring their sensitive or proprietary information is securely managed across platforms and departments. To protect client data, businesses should choose AI providers with robust security measures and transparent data practices that safeguard information at every stage. 

6. Societal & Environmental Wellbeing 

In an age of climate awareness, it is also essential to consider AI’s environmental footprint. AI can be energy-intensive, so opting for energy-efficient solutions internally or partnering with external providers with green practices is important. Environmentally responsible AI solutions are those that help solve business challenges in a sustainable way that respects our planet’s resources.  

7. Accountability 

Accountability ensures that companies remain responsible for their AI tools’ actions. Having clear processes to audit and evaluate AI decisions is essential. This way, businesses can address issues, learn from them, and continue to improve. Accountability in AI use demonstrates to users and stakeholders alike that you take responsibility for your actions. 

A Final Thought for Businesses 

Ethical AI implementation is not just about staying ahead in innovation; it’s about doing so in a way that upholds trust, transparency, and inclusivity. By following these principles, you will be well-positioned to implement AI responsibly, thus making a positive impact on both your clients and society as a whole. For industries like translation, healthcare or pharmaceuticals in which data security and cultural sensitivity are paramount, adhering to ethical AI principles helps deliver not only reliable results, but also peace of mind. 

How to Navigate Ethical Concerns in AI
11/02/2025

How to Navigate Ethical Concerns in AI

Posted on | by Jacqueline De Marco | Leave a Comment on How to Navigate Ethical Concerns in AI

The use of any technological tool requires a full understanding of its functioning and potential limitations and risks. In the translation industry, irresponsible or unethical use of Artificial Intelligence (AI) poses significant threats, which can lead to serious consequences for businesses, communities, and language integrity itself. In this blog post we will address some key ethical concerns associated with the use of AI and provide practical steps to ensure its responsible use. 

Ethical Concerns 

There are a few different ethical concerns you will want to keep top of mind before using AI.  

1. Copyright and Ownership Concerns 

To properly train generative AI, you need a large amount of data. The source of that data is often public postings on the internet. This can lead to the inclusion of copyrighted material without proper authorization. The legal situation around the use of generative AIs is complex and often undefined. There is a significant risk of inadvertently using AI-generated content that may contain copyrighted elements, which could lead to legal complications. Therefore, it’s crucial to check the output for its origin and ensure it does not violate existing copyrights. 

2. Bias 

Generative AI models are trained on human-created content, which often includes inherent biases. As a result, AI sometimes produces biased, stereotyped, or even harmful outputs. While developers are working to mitigate these issues, achieving unbiased results is a challenging endeavor that may take years to perfect. Unlike humans, machines struggle to understand nuanced contexts, which makes it difficult for them to consistently produce fair and unbiased content. 

3. Confidentiality 

Given that AIs are trained on vast datasets, privacy concerns naturally arise. Issues like data breaches, unauthorized access, and improper collection, storage, and use of information are significant risks. Many AI systems are public-facing tools accessible to anyone without restrictions, which poses a considerable threat to confidentiality. Information inputted into these systems can potentially be used to generate responses to future queries, risking exposure of sensitive data. 

Practical Steps for Ensuring Ethical AI Use 

If you intend to use AI, these are some steps you can take to do so ethically.  

1. Check that AI Tools Adhere to Ethical Standards 

Before adopting any AI tool, ensure that it was developed with the four pillars of ethical AI in mind—privacy and security, transparency, fairness, and accountability. These pillars can guide your decision-making process and help you choose tools that align with ethical standards. 

2. Verify Data Sources 

AI tools often present information with equal certainty, regardless of its accuracy. It’s essential to fact-check their output for truthfulness and sound reasoning. You also need to confirm the content is ethically sourced and does not infringe on copyrights. Human oversight is crucial to avoid liability claims and ensure content accuracy. 

3. Develop and Update Company Policies 

Create and regularly update company policies regarding the use of AI in the workplace. Train employees on these policies to ensure they understand the ethical considerations and potential risks associated with AI use. Provide guidance on mitigating issues such as misinformation, privacy breaches, ethical and legal challenges, and bias. 

4. Review AI Outputs 

Review AI-generated content to ensure it respects cultural sensitivities and norms. Be aware of regional differences and gender sensitivities to avoid producing content that could be offensive or inappropriate. 

5. Consider Closed AI Systems 

For handling sensitive materials or information, consider using a closed AI system that is trained solely on your company’s data. This approach prevents your input from being used in public-facing AI models, helping to safeguard your confidential information. 

6. Maintain Transparency with Clients

Transparency is key to building trust with your clients and stakeholders. Keep them informed about the AI tools you use and the measures you take to ensure ethical practices. Being open about your use of AI and your commitment to ethical standards will enhance your credibility. 

The Takeaway 

The ethical concerns surrounding AI are critical issues that businesses must navigate as they integrate these technologies. It is paramount to ensure a solid and trustworthy AI-human collaboration, maintaining ethical standards to protect your business, clients, and community. Responsible AI use not only prevents potential legal and ethical pitfalls but also strengthens your reputation and trustworthiness in the industry. 

By understanding and addressing these ethical concerns, companies can harness the benefits of AI while minimizing risks and fostering a more responsible technological future. 

The New Role of Translators in the AI Era
28/01/2025

The New Role of Translators in the AI Era

Posted on | by Jacqueline De Marco | Leave a Comment on The New Role of Translators in the AI Era

In recent years, the rapid advancements in artificial intelligence (AI) have significantly transformed a variety of industries. From automating repetitive tasks to streamlining complex workflows, its impact is broad and undeniable. The translation industry, in particular, felt this shift as AI-powered tools begin to shape how translation services are managed and executed. While these innovations are revolutionizing the field, they are also transforming the role of translators, boosting productivity and creating a need for new skills and expertise. 

The Evolution of the Translator’s Role 

Translators have always adapted to technological changes, from the rise of computer-assisted translation (CAT) tools and translation memories two decades ago to the more recent integration of machine translation systems. Now, AI has entered the scene, promising to streamline translation processes further. But rather than replacing human translators, AI is becoming part of an evolving toolkit that enhances the human touch. 

Today, translators are shifting away from purely linguistic tasks and are now managing AI-driven tools that combine translation memory, machine translation, and terminology databases. These advanced platforms enable translators to work more efficiently, maintaining consistency across large-scale projects. Instead of focusing on line-by-line translation, professionals are increasingly tasked with refining and contextualizing AI-generated content, especially in areas where AI struggles to capture cultural nuances and idiomatic expressions. 

While this new technology can handle the bulk of a translation, the human touch remains essential in ensuring the final product resonates with local audiences. This dynamic underscores the emerging partnership between AI and human translators, where technology augments human capabilities rather than replacing them entirely. 

A Radical Shift in Skills 

As the industry transforms, so too must the skill set of professional translators. Familiarity with AI and machine learning is becoming increasingly important, and translators are now expected to understand how to train and manage AI translation models. In addition to linguistic expertise, they must develop technical skills, ranging from using AI tools to analyzing data that informs the translation process. 

Moreover, the human element of translation remains crucial. Cultural sensitivity, deep contextual understanding, and the ability to adapt content for specific audiences are skills that machines cannot yet master. Translators who enhance their proficiency in these areas, along with the technical aspects of AI, will find themselves better positioned to thrive in this new landscape. 

Adapting to Change: A Necessity for Translators 

In a rapidly evolving industry, adapting to new technologies is vital. Those hesitant to embrace these innovations may find themselves falling behind as AI continues to revolutionize workflows. Conversely, translators who engage in ongoing professional development and learn how to integrate AI into their processes can increase their value to clients and extend their capabilities. 

Embracing change allows translators to not only maintain their place in the industry but also enhance their work quality and expand the scope of their services. Those who adapt will emerge as skilled professionals capable of balancing technology with the irreplaceable human touch. 

The Takeaway 

The world of translation is evolving quickly, but rather than viewing AI as a threat, it should be seen as an opportunity for growth. Human translators will always be essential in providing the cultural and contextual knowledge that machines cannot replicate.

Neural Machine Translation vs Large Language Models
26/11/2024

Neural Machine Translation vs Large Language Models

Posted on | by Jacqueline De Marco | Leave a Comment on Neural Machine Translation vs Large Language Models

Artificial Intelligence (AI)-powered tools have become integral in various industries, and their influence is becoming more and more prominent in translation and localization. Two notable AI-driven technologies in this field are Neural Machine Translation (NMT) and Large Language Models (LLMs). While both are powerful tools, understanding their differences is essential as their applications, underlying architectures, and functionalities have distinct strengths and weaknesses. This knowledge helps professionals choose the right tool for their specific needs, optimizing efficiency and accuracy in language-related tasks. 

What is Neural Machine Translation and How Does it Work? 

Neural Machine Translation (NMT) is an advanced AI technology designed to automatically translate text from one language to another. Unlike traditional translation methods that rely on predefined rules, NMT employs a neural network—a computer program that improves translation accuracy by considering the entire text’s context and learning from vast amounts of example data. 

NMT systems typically have two main components—one that reads and understands the original text and another that generates the translated text in the target language. This process mimics the human brain’s function, using interconnected nodes that enable the model to learn and enhance its capabilities over time. The ability of NMT systems to learn from context allows them to provide more fluent and coherent translations compared to older translation methods. 

What are Large Language Models and How Do They Work? 

Large Language Models (LLMs) are sophisticated AI systems designed to understand and generate human-like text. They are trained on vast datasets of text, which enables them to perform a wide range of language-related tasks beyond translation, such as text generation, summarization, and conversational AI. 

LLMs use deep learning techniques with multiple layers of neural networks. Each layer refines the model’s understanding of the data, employing an attention mechanism that focuses on specific parts of the input data. This process allows LLMs to generate text by predicting the next word in a sequence based on the input they receive, making them versatile in generating coherent and contextually relevant text. 

Pros and Cons of NMT and LLMs 

No technology is perfect, so let’s take a look at both the advantages and disadvantages of NMT and LLMs.  

Pros of NMT: 

  1. Improved accuracy: NMT systems provide more accurate translations by considering entire sentences or paragraphs, reducing errors common in traditional methods and resulting in more natural and coherent translations. 
  1. Customization: Users can fine-tune NMT outputs by incorporating specific terminology databases, brand-specific glossaries, and other data sources, further enhancing the relevance and correctness of translations. 
  1. Integration versatility: NMT can be easily integrated into various software applications via APIs and SDKs and supports numerous content formats, including CAT (Computer-Assisted Translation) tools. 
  1. Continuous improvement: NMT systems constantly evolve by learning from new data, adapting, and improving translation quality over time. 

Cons of NMT: 

  1. Lack of cultural awareness: Despite considering context, NMT can still produce inaccurate translations and lacks the ability to make nuanced decisions based on cultural contexts or idiomatic expressions. 
  1. Data dependency: NMT requires vast amounts of data for training purposes, which can be challenging for less common languages or specialized fields. This can lead to less accurate translations for rare language pairs or niche terminology. 
  1. Bias in outputs: Like all AI, NMT systems can inherit biases from their training data, leading to biased outputs in translation regarding gender, occupation, and other sociocultural factors. 

Pros of LLMs: 

  1. Versatility: LLMs can handle a broad spectrum of language-related tasks beyond translation, including text generation, summarization, and dialogue systems. 
  1. Human-like text generation: LLMs can generate human-like text, making them highly valuable for content creation and applications requiring natural language understanding. 
  1. Customization through fine-tuning: Companies can tailor LLMs to align with specific needs and objectives through additional training and fine-tuning, enhancing their utility across various applications. 

Cons of LLMs: 

  1. Lack of reasoning: LLMs often generate text based on probabilistic guesses rather than genuine reasoning, which can lead to inaccuracies or irrelevant responses. 
  1. Privacy and data concerns: The data usage and privacy practices involved in training some LLMs are under scrutiny, raising ethical and legal concerns about data privacy. 
  1. Susceptibility to manipulation: LLMs can be manipulated to generate fake or misleading content, posing risks, especially in sensitive applications like social media and news. 

The Takeaway 

NMT and LLMs represent significant advancements in artificial intelligence, revolutionizing how we approach translation and other language-related tasks. While these tools offer substantial benefits, it’s crucial to view them as complementary to human expertise, particularly in ensuring accuracy, cultural sensitivity, and nuanced understanding. Professionals should leverage these technologies to enhance their workflows while remaining vigilant to their limitations and the importance of human oversight.

04/07/2023

ChatGPT and the Importance of Linguistic Diversity

Posted on | by Jacqueline De Marco | Leave a Comment on ChatGPT and the Importance of Linguistic Diversity

In a world where linguistic diversity is the norm, and as our digital society continues to evolve and expand, it is paramount that tools like ChatGPT (which we talked about in a previous blog post) embrace diversity and inclusivity to create a more meaningful and impactful online experience. In this blog post, we will explore the significance of linguistic diversity and the challenges and opportunities that come with incorporating language diversity into AI tools like ChatGPT.

Understanding Language Diversity

Language diversity refers to the rich and varied ways in which people communicate across different linguistic backgrounds, dialects, and accents. It encompasses the vast array of languages spoken worldwide, from the world’s biggest languages like English and Mandarin to smaller languages spoken by just a few thousand people.

Moreover, language diversity also includes cultural nuances and idioms that reflect the unique values, beliefs, and practices of speech communities. For instance, in some cultures, direct communication and assertiveness are highly valued, while in others, indirect and polite speech is preferred.

Recognizing and respecting language diversity is crucial in the development of AI tools that aim to communicate with people from different linguistic and cultural backgrounds. Failure to account for linguistic and cultural differences can result in AI algorithms that are biased, insensitive, or even offensive.

Moreover, AI developers must strive to incorporate different dialects, accents, and cultural nuances in their models to ensure that their algorithms can correctly interpret and produce responses that don’t sound robotic or too unoriginal. This requires a deep understanding of different linguistic and cultural practices and the ability to adapt language models to meet the needs of diverse populations.

Challenges and Opportunities

The development of AI models like ChatGPT has the potential to revolutionize how we communicate, but incorporating language diversity presents many challenges. One of the most significant challenges is data bias. Language models learn from vast amounts of data, and if that data is biased, the AI model can perpetuate that bias. This can result in inaccurate or offensive language generation, which can be harmful to individuals and communities.

Developing AI models for less commonly spoken languages or dialects is also challenging due to limited resources (like data sets, language experts, and research). There may not be enough data available to train language models accurately, and linguistic features unique to a particular language or dialect may be difficult to incorporate seamlessly. This can result in language models that are inaccurate or incomplete, which can limit their usefulness.

Furthermore, the accuracy and quality of language generation can be impacted by variations in grammar, syntax, and vocabulary. Languages can vary greatly in these areas, making it challenging to develop AI models that can accurately generate language across different languages and cultures. This can result in language models that are not effective in communicating with a diverse range of people as their outputs are too standard. If you ask a chatbot to produce a summary of a text, it typically won’t incorporate the linguistic uniqueness of the community the content will be used in. While it can produce text in the language needed, the richness of the language is lost because it can’t reproduce it (it was not trained on that).

Cultural sensitivity is another significant challenge when incorporating language diversity into AI tools like ChatGPT. AI models may generate language that is culturally insensitive or offensive due to a lack of understanding of cultural nuances, customs, and beliefs. This can result in language models that are discriminatory or cause harm to individuals or communities.

Finally, incorporating multiple languages and dialects into AI tools like ChatGPT can be complex and require significant resources and expertise. AI models that can generate language across multiple languages and cultures must be carefully designed to ensure accuracy and avoid cultural bias. This requires a deep understanding of linguistic and cultural differences and a commitment to diversity and inclusion.

In light of these challenges, it’s also important to look at the potential benefits of incorporating language diversity into AI tools like ChatGPT. Incorporating language diversity into AI models is a critical step toward achieving more accurate, relevant, and culturally sensitive chatbots. AI models that incorporate diverse languages have the opportunity to exhibit better performance when it comes to sentiment analysis, converse fluently, and more effectively handle the diversity of expressions and tones of emotions specific to multilingual users. In addition, language diversity is beneficial when it comes to reducing machine bias and implementing more ethical AI.

The Takeaway

For people designing and developing these tools, it’s important to prioritize linguistic diversity and not leave behind other languages and dialects, so that the richness of language is not lost. For users worldwide, it’s important to understand how these tools work, identify their weaknesses, and use them responsibly. There’s nothing wrong with making use of their perks, but the human intellect and unique touch will never be replaced.

Does machine translation reinforce gender bias - Portada
21/06/2022

Does machine translation reinforce gender bias?

Posted on | by Jacqueline De Marco | Leave a Comment on Does machine translation reinforce gender bias?

Although a machine learning model can be a powerful tool in the translation space, it can only be as good as the data it learns from. If there is a systematic error in the data used to train a machine learning algorithm, the resulting model will reflect this. These errors are the main reason that gender bias is present in machine translation (MT). Some aspects of this are out of the control of the machine translation engine creators, but some others aren’t. Let’s examine how MT reinforces gender bias and how it can be fixed.

How Errors Can Occur

Wikipedia serves as a good example of how machine translation errors can occur and reinforce gender bias. Wikipedia’s entries tend to be geographically diverse, lengthy, and refer to subjects in the third person, which leads to the use of a lot of pronouns. Therefore, these entries (particularly biographies) often have potential to cause machine translation errors related to gender, especially if an article refers to a person explicitly early in a sentence, but not later on. 

How Errors Can Be Resolved

Let’s look at Google as an example of a company aiming to resolve machine translation mistakes regarding gender. Google acknowledges that its translation tools struggle with errors that lead to reinforcing gender bias. They believe that they need to advance translation techniques to surpass single sentences. Doing this requires setting new metrics for measuring their progress and creating datasets with the most commonly encountered context-related errors. They’re facing a significant challenge. Translation errors related to gender are susceptible, as they can incorrectly refer to someone and how they self-identify. 

Google is working towards long-term improvements on their machine learning systems so they can continuously improve how they translate pronouns and gender.

The Takeaway

In recent years there’s been more awareness that these biases exist and machine translation engineers are trying their best to resolve this issue quickly. However, it’s no easy endeavor since gender works so differently in all languages. Even though many advancements have been made in the machine translation industry, work still needs to be done. In all reality, a human translator is much better equipped to handle such sensitive issues like gender. 

It has taken many years to improve machine translation quality, and additional improvements will take more time to make. However, addressing this issue can’t wait that long. Errors that reinforce gender bias are critical to work on now, considering the recent relevance gender inclusivity has taken recently. If a company wants to prioritize inclusive language, it’s not safe to use an automated solution. Gender is a sensitive topic; with a translation, you want to ensure you convey the message discreetly. Right now, human translators are researching and staying up to date with the latest trends in the languages they work with. This is necessary, as everything is changing so fast. Companies should turn to these professionals to ensure their brand doesn’t lose impact by a careless machine translation mistake.