machine translation

The future of termbase management

18.4.2024 by Delingua

As we have seen recently, things can change very quickly in business technology. AI has leaped into our lives and is now becoming a major part of most of the daily processes and systems in ways we could never have imagined a year ago.

Termbases, critical business tools for those who seek quality and consistency in the content, and their related science of terminology management will be no different.

Peering into our crystal ball let’s see if we can catch a glimpse of a few of the future trends that will be impacting on our termbase management systems.

Cloud network storage

Long gone are the days when termbases were confined to a single desktop, accessible only by the one who created it. With cloud computing becoming much more ubiquitous in business, we can expect to see termbases becoming not just about storage but about accessibility, availability, and real-time updates. As businesses become increasingly global, the need for language experts to access these linguistic assets from anywhere, be it a bustling office in London or a quiet cafe in Kyoto, becomes imperative.

Cloud platforms don’t just facilitate this global reach; they also ensure that backup, security, and updates are managed centrally, reducing the nitty-gritty tasks for businesses, and freeing them to focus on their core operations. It also allows for greater levels of integration with more applications ensuring that you can apply your business language to your content, at anytime and anywhere.

Automation

Imagine a world where the process of updating a termbase doesn’t require manual intervention but is instead a seamless, automatic process. That’s where automation steps in.

Instead of combing vast amounts of content to identify and add new terms, automation tools, driven by AI, can detect, classify, and even suggest terms for inclusion. This not only streamlines the process saving you time and money, but also reduces the chances of error that manual processes can introduce therefore producing a much higher-quality final product.

AI data enhancement

Large Language Models thrive on data. The richer and more diverse the data, the more adept these models become by training the model with your specific business language. Termbases, teeming with linguistic intricacies, provide a golden opportunity here. By integrating termbases into AI training data, businesses can ensure that their AI tools are not just linguistically aware but are also culturally and contextually astute.

For businesses aiming for global resonance, this is a game-changer. A chatbot trained with a comprehensive termbase, for instance, would not just communicate; it would connect, understand nuances, and deliver a richer customer experience. Generative AI models would be able to produce content in your business voice using correct product names and ensuring consistency across the board.

Collaborative platforms

The beauty of collaboration is that it captures a myriad of perspectives. In the context of termbases, this means a more robust and diverse linguistic database. Collaborative platforms are set to revolutionise termbase management by enabling teams, irrespective of their geographical location, to contribute to, refine, and leverage a centralised termbase.

This collective approach ensures that the linguistic spectrum captured is broad, nuanced, and immensely valuable and specific to your business needs.

Looking forward in termbase management

The great news is that many of these exciting changes to greater improve and simplify termbase management are already here and improving day by day. You just need to understand and take advantage of them.

This article was originally published by LanguageWire Company (Verity Hartley)

Artificial intelligence – the latest localisation superpower

22.3.2024 by Delingua

Large language models – what are they?

13.3.2024 by Sohvi Kähärä

Over the past year, it has been difficult not to come across content created by generative AI.You may have seen a LinkedIn post where OpenAI’s AI-based ChatGPT has been asked to give good tips on how to get a salary raise. Perhaps you have encountered a chatbot when you have needed help with booking an appointment online. You may have even laughed at images created by DALL·E, where the faces of familiar celebrities have been digitally added to classic works of art.

Generative AI is developing at a tremendous pace and language technology is also benefiting from these leaps of development. From the perspective of language technology, the most important role is played by large language models (LLMs).

Large language models need massive amounts of data

Generative AI solutions created for producing and processing language are called large language models. They are statistical models that calculate the probabilities of the occurrence of words or parts of words. The simplest language models calculate the probability simply by calculating how many times the word appears in the given text materials, without taking the surrounding words and their meanings into account.

Large language models, on the other hand, are advanced language models that are based on neural networks and, as their name suggests, are trained with large amounts of data. The training of the language models of previous neural machine translation engines often requires a dataset of hundreds of millions of variables but large language models require billions of them.

Current neural network-based language models, including large language models, are most often based on transformers. Transformers are neural network architectures that consist of many overlapping layers. Previous machine learning models focused on individual words in their training data, while transformers are capable of simultaneously processing and taking into account words within a sentence or a longer text and analysing, among other things, broader relationships within the sentence.

Large language models use huge text materials to learn which words appear in which context and which words often follow each other. This allows large language models to predict what words and phrases would appear in an answer suitable to a certain type of question, how a beginning of a poem could continue or how a certain matter would be expressed in another language in the context in question.

A one-size-fits-all solution is not suitable for all cases

Since the amount of data needed for training large language models is enormous, a lot of generic content will inevitably have to be used. Large language models can also be customised, or personalised, to make them more suitable for different tasks and needs. This can be done by adding additional training data in a more limited subject area and modifying the model’s weightings. It is also important to choose a suitable customisation strategy, such as building a ready-to-use model or learning through use with the aid of prompt engineering.

Especially when large language models are used for translation, it is useful to personalise language model with high-quality, fully matching bilingual materials. It is especially good if the texts used in personalisation and the new texts to be translated are from as similar subject areas as possible. For this purpose, training language models with existing translation memories is a good idea: all of the customer’s translations are saved in translation memories by language pair so these texts are always bilingual, are good matches and use the company’s desired style and terminology.

In this way, the language model learns what the customer’s texts are like and is able to imitate their style better in the future. This improves, among other things, how machine translation produced by large language models is aligned with the company’s style. This, in turn, speeds up translating as higher-quality machine translation that is customised according to the customer’s needs reduces the time involved in any post-editing required.

The downsides of AI

Although continuous learning and adaptability associated with large language models are in many respects good, they also have their downsides. Language model-based solutions in general use learn as they are running so they can also be intentionally misled and taught false information. If you repeatedly teach a language model that a pike is a bird instead of a fish, it will start believing this to be true. Large language models also suffer from so-called hallucinations, which you can read more about in our article on getting the most out of machine translation.

In addition to invented facts, another risk associated with language models trained with a large amount of data is that confidential information ends up in the wrong place. Large language models in general use are open to everyone and, when using them, it is not possible to be sure whether your data will be made available to others. Therefore, it is important to choose a reliable partner for whom data security issues are a priority.

When training large language models with translation memories, it is necessary to take care of data security and ensure that customer-specific data is used only in each customer’s own model. This way, you can avoid the situation in which the language model would introduce another customer’s confidential information into the translation. We can build a customer-specific language model directly as part of LanguageWire’s highly secure ecosystem. Read more about LanguageWire’s insights into the data security in large language models here.

Large language models as part of the future

Training large language models for translation needs is a giant leap forward as they can provide more accurate and natural translations into multiple languages. These models make it possible to produce translations quickly while taking into account the desired style and terminology. In the future, this development can break down language barriers and foster interaction between people around the world by strengthening understanding and cooperation on a global scale.

What is generative AI?

29.1.2024 by Delingua

We’re on the cusp of a technological revolution. One that promises to change how we research, work, and even live. 2023 was the year of Generative AI.

Generative AI isn’t just a buzzword; it’s a powerful force that has the potential to transform the way we communicate with each other and create content. It’s like having a creative genius, a tireless assistant, and a subject matter expert at your fingertips, ready to help your business thrive in an era where innovation is the name of the game.

So, whether you’re an industry leader looking to revolutionize your business or simply someone curious about the many possibilities of technology, get ready to discover just what exactly Generative AI has to offer.

Let’s dive in.

What is Generative AI?

Generative AI, short for Generative Artificial Intelligence, is a cutting-edge technology that empowers machines to autonomously create content across various domains. Unlike traditional computer programs that follow predefined rules, Generative AI models are designed to simulate human-like creativity, decision-making, and problem-solving processes.

At the heart of Generative AI lies the concept of machine learning, particularly deep learning, a subset of artificial intelligence. These AI models are capable of not only processing and analysing vast amounts of data but also generating new content based on the patterns and knowledge they’ve gleaned from the data they are trained on.

Versatility of Generative AI

Generative AI is incredibly versatile and can be applied in a variety of different ways to aid content creation and research. For example:

Content Generation: Generative AI can create written content, including articles, blog posts, product descriptions, and marketing materials. It can generate text in different styles, tones, or languages, making it a valuable tool for content marketers and copywriters.

Art and Design: In the creative world, Generative AI can produce art, design concepts, and even music compositions. Artists and designers can use AI-generated content as a source of inspiration or to augment their existing creative processes.

Simulation and Prediction: In industries such as finance and logistics, Generative AI can simulate scenarios, predict outcomes, and provide recommendations. This assists businesses in making data-driven decisions and allows data analysts to optimise their workflows.

Personalisation: Generative AI analyses user data to tailor content and recommendations. For instance, e-commerce platforms use it to suggest products based on a customer’s browsing and purchase history, enhancing the shopping experience for a user.

Language Translation: Language models like GPT-3 can translate text from one language to another with a high level of accuracy in many language pairs and can be further personalised with business-relevant language using termbases and translation memories as training data.

How Does Generative AI Work?

At its core, Generative AI relies on the power of large datasets and complex algorithms. These AI models are trained on massive amounts of data, learning to mimic human creativity and decision-making processes. For instance, OpenAI’s ChatGPT, currently the most well-known Generative AI model, has been trained on a diverse range of internet text, making it capable of producing high-quality content across various domains.

Generative AI operates on a blend of advanced algorithms, vast datasets, and deep learning techniques. Let’s delve a little deeper into the technicalities of Generative AI to help you understand its fundamental mechanisms.

1. Data and Training

As we said above, the core of Generative AI is the process of training. Imagine it as teaching a machine to understand and generate content by exposing it to a massive amount of data. For instance, in the case of language generation, an AI model is trained on vast collections of text, encompassing everything from books and articles to websites and social media posts.

2. Neural Networks

The central component of Generative AI is a neural network, which mimics the structure and function of the human brain. Neural networks are composed of interconnected nodes, or artificial neurons, organised in layers. There are typically three types of layers in a neural network:

Input Layer: This is where data is initially fed into the network.
Hidden Layers: These intermediate layers process and transform the data.
Output Layer: The final layer produces the desired output, which, in the case of Generative AI, is the generated content.

3. Learning and Weights

During training, the AI model learns the relationships and patterns within the data by adjusting the strengths of connections between artificial neurons. These connection strengths are referred to as “weights.”. The learning process involves iteratively adjusting these weights to minimise errors and make the model better at predicting or generating content.

4. Generating Content

Once the AI model is sufficiently trained, it can generate content by taking an initial input, often referred to as an “input” or “prompt,” and processing it through its neural network. The model then leverages the patterns and associations it has learned from the training data to produce coherent and contextually relevant output.

5. Fine-Tuning

To make the generated content even more accurate and tailored to specific tasks, businesses often engage in a process called “fine-tuning.” This involves training the model on a narrower dataset that aligns more closely with the desired application. For example, a company might fine-tune a Generative AI model on its product descriptions to ensure they match its brand style and tone.

How Businesses Can Leverage Generative AI

Generative AI’s ability to create content autonomously is a game-changer for businesses. It can automate content generation, reduce operational costs, enhance personalisation, and accelerate innovation.

The business advantages of Generative AI are multifaceted. Firstly, it offers cost-effective content generation. Imagine automating the creation of product descriptions, social media posts, or even legal documents. This not only saves time but also reduces operational costs.

Moreover, Generative AI enhances personalization and customer engagement. By analysing user data, it can generate tailored recommendations, advertisements, and even chatbot responses, leading to higher customer satisfaction and loyalty.

Additionally, Generative AI accelerates product development. It can assist in generating design concepts, and prototypes, and even simulate product performance under different conditions, helping businesses bring new products to market more quickly.

The benefits and use cases for specific industries are still being explored but the potential of this new technology seems limitless. For example, the healthcare industry has already started utilising Generative AI for drug discovery, predicting disease outcomes, and even generating medical reports.

Concerns and Challenges Surrounding Generative AI

While Generative AI offers tremendous potential for businesses, it’s essential to address certain concerns and challenges to ensure responsible and ethical usage rather than blindly embracing new technology without proper scrutiny. There are some issues you need to consider before implementing Generative AI into your processes.

Ethical Considerations

Generative AI can sometimes produce content that is misleading, offensive, or biased. This raises ethical concerns, especially when businesses use AI-generated content in customer-facing communications or decision-making processes.

Privacy and Data Security

Generative AI relies on extensive datasets, and using this data responsibly is critical. Mishandling data can lead to privacy breaches and regulatory violations, potentially damaging your business’s reputation.

Regulatory Compliance

AI, especially in highly regulated industries like finance and healthcare, must comply with a myriad of regulations. Ensuring that AI-generated content meets these standards can be challenging.

Training Data Quality

The quality and representativeness of training data can influence the effectiveness of Generative AI. Biased or incomplete data can lead to biased or inaccurate AI outputs.

Generative AI holds immense promise for businesses, but it also presents challenges that must be addressed responsibly. By implementing ethical guidelines, prioritising data security, defining accountability, and remaining vigilant, businesses can harness the power of Generative AI while minimising risks. The key is to strike a balance between innovation and responsibility, ensuring that AI serves as an asset to your organisation without compromising ethics and integrity.

The Future of Generative AI and Language Service Providers

At LanguageWire we have already talked in depth about how we foresee Large Language Models being the future of not only content generation but also machine translation.

Embracing this new technology will be the next step in innovation in this field. The field of Generative AI is constantly evolving, and we anticipate even more remarkable developments in the future.

This article was originally published by LanguageWire (Verity Hartley)

5 useful ways technology can optimise your translation project management workflow

12.10.2023 by Delingua

Translation project management is the art of keeping lots of different pieces in motion at the same time. Stakeholders, third-party vendors, project deadlines and budgets all need to be kept in check while you ensure that your translations are delivered to the highest possible quality.

Can technology make translation project management easier? Yes. Absolutely.

Gone are the days when you were expected to keep track of all your translations in a spreadsheet. Technology has evolved to the point that you can use it to make your job easier, ensuring that you can produce translations on time and of the highest quality when you need them.

Let’s look at how technology can help you improve your translation project management processes.

Problem: Too Many Applications, Emails, Documents…

Translation project management is complex.

You need to keep lots of different plates spinning throughout the lifecycle of a project. You are constantly maintaining open communication with numerous stakeholders, managing different versions of translations from linguists and validators while also keeping on top of budgets and tracking deadlines.

It can be difficult to stay on top of where everyone is throughout the project workflow, and you need to keep switching in and out of your inbox and spreadsheets to make sure everything stays on track.

Solution: A Translation Management System (TMS)

A translation management system (TMS) will bring all of your stakeholders, file versions, notes and planning documents into one centralised location. With this software tool, you can stop hopping back and forth between applications and inboxes to minimise the risk of a loss of data and quality in your translations. Keep it simple by managing the entire project in one place.

Problem: Manual Processes

If one of your translation project stakeholders is a web manager or a product manager there is a very good chance that you will also be familiar with the tools that they use.

A web manger uses a Content Management System to keep the website updated. When creating multilingual versions of a site they will need to import translations quickly and easily. This is also the case with product and asset management software.

Importing translations can be a slow process when done manually and require a lot of oversight to ensure no errors are made in the process.

Solution: API & Custom Integrations

Luckily thanks to APIs and digital connectors within most modern content and assets management tools, this process can become fully automatic. Simply connect your application to a tech-enabled translation management tool and your translations can be imported directly from the translator to the page.

Problem: Translations Are Slow

Translating a document accurately into another language so that it retains its message but still resonates with other cultures is a skilled and time-consuming process. A linguistic expert needs to take time to make sure they choose the right words and phrases to match the cultural expectations of the target language. The job cannot be rushed if you want the quality of your translations to remain high. However, this can prove challenging with tight deadlines and a need to beat your competitors into multilingual markets.

Solution: Post-Edited Machine Translation

Machine translation has come a long way over the last few decades and is now a key tool for linguists to use in their translation process. Machine translation can translate large sections of text in moments and even use Translation Memories to accurately recall previously translated sections and automatically insert them into the content.

With post-editing by a human linguist to add their expertise and ensure that the translation is of the highest quality, machine translation can greatly speed up the entire workflow.

Problem: Maintaining Translation Consistency

When you work with lots of different stakeholders, including many different translators, it can be difficult to maintain a high level of quality. Words and phrases can be translated in lots of different ways and the final decision can be subjective. One vendor may choose a different term to your preference, and it can take a lot of time and effort to validate that everything is matching your corporate voice.

Solution: Termbase

A controlled Termbase will help with this. A Termbase is a set of the terms that you want for your translations. The Termbase will highlight which translation choices do not match your preferred terms added to the Termbase and make suggestions for the translator to use instead. This helps you maintain quality control over your multilingual content.

Problem: Data Risks

When contacting and working with vendors outside of your computer network you open yourself up to the risk of data loss. If you are translating documents that are highly sensitive, they could present a risk to your company when sent outside of your network. The same may be true for other sensitive data such as translation memories and your termbase. If your vendor’s, IT equipment is compromised you could be at considerable risk as well as breaching various data protection protocols.

Solution: Bring Your Vendors into Your System

Within a closed system protected by the highest level of information security protocols your vendors and your data are safely hidden away from any would-be data thieves.

In a secure translation management system that allows collaboration behind a security network you can be sure that you will be able to complete your project securely and limit the risk of any data loss.

LanguageWire Helps Bring Technology into Your Translation Project Management Process

Technology is quickly changing the way we live and work. We can leverage these technologies to aid our working life and free us up to focus on things that really matter. Whether you want to work more efficiently, quickly, cost effectively or securely on your translation projects we have a solution to help you.

This article was originally published by LanguageWire Company (Verity Hartley)

Top 3 benefits of automating your manual processes

29.8.2023 by Delingua

The rapidly changing pace of technology can be frightening, and sticking to manual processes can give us a false sense of control over our daily work.

It can be hard to let go of things that we do manually as we feel like we can always do it better ourselves. However, this is not always true. Technology can automate processes that support us and actually make our days more efficient and effective.

Read on to learn more about how automating your manual processes when working on multilingual projects can actually be a huge benefit.

1. Automation Minimizes Errors

As much as we hate to admit it, humans make mistakes. They can not be helped. We get tired, emotional and unwell, which can cause our focus to slip. In manual processes, small mistakes can slip through the cracks, but their impact can be felt on a much grander scale.

In 2005, the Japanese company Mizuho Securities lost over $345 million when a tiny typo sold 610,000 shares for 1 yen instead of 1 share for 610,000 yen.

Automating common manual processes can help reduce that feeling of cold dread creeping in when a translation error or typo is noticed.

Machines do not make mistakes once they are configured correctly. They will run through the same process an infinite number of times and never click the wrong button or attach the wrong file. They also follow any compliance regulations you require to the letter of the law.

Automating regular manual processes can reduce the number of mistakes made and decrease your stress levels at the same time.

2. Automation Saves You Time

Manual processes, by their very nature, are labour-intensive. They require a human to see them through from beginning to end, and it’s highly unlikely that the rate of processing will remain consistent throughout the workflow as we all naturally tire throughout the day.

Once configured to the correct workflow, a machine can drastically cut the amount of time required. Translations can be done immediately by AI. Connectors can instantly send pages for translation and replace the original copy once completed. With automation, you can get your content to market quicker than those still working with a manual process.

All of this means more free hours for you to focus on other aspects of your job that machines can not do, like building relationships and engaging with people. Your translations will be exactly where you need them to be when you get back, thanks to automated technology.

3. Automation Saves You Money

Automation can impact your bottom line in lots of positive ways. It gives you more time to focus on your business. It can also reduce operating costs when technology can complete a process that took several people to complete in the past.

You can decrease lead times for getting your products to market and see a faster ROI for doing so. This makes you more competitive, allowing you to beat your competitors into new global sectors.

Quality is increased, so you need to spend less on repeating work or fixing mistakes. Automation helps with efficiency, allowing you to dedicate your employee’s time to more important tasks.

The Benefits of Automation

Automation improves quality and efficiency while saving you time and money. It allows technology to take the strain so you can focus on what matters to you, your business.

Hopefully, this article has shown you that it is time to let go of your manual processes and start thinking about automation within your multilingual projects.

This article was originally published by LanguageWire Company (Verity Hartley)