language technology

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

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)

The importance of linguistic quality assurance in translation

10.1.2024 by Delingua

What is the true price of linguistic quality?

In today’s global marketplace, it is increasingly important for businesses to be able to accurately communicate with customers, partners, and employees from across the globe.

Poor quality translations could cost your business in revenue, access to global markets, business reputation and in some worst cases scenarios even result in physical harm or loss of life.

Linguistic Quality Assurance (LQA) is an essential component of the end-to-end translation process. It plays a key role in making linguistic quality a measurable and achievable goal by ensuring that translations consistently meet the necessary defined standards protecting you, your customers, and your business.

In this article, we will dive a little deeper into how Linguistic Quality Assurance (LQA) can help your business take the quality of your translated content to the next level.

What is Linguistic Quality Assurance (LQA)?

Linguistic (or Language) Quality Assurance (LQA) is a system of processes for evaluating and verifying the quality of translations across a project. It ensures that the translations are accurate, clear, and convey the intended message. LQA should always be performed by professional linguists who are also subject-matter specialists. They must be experts in target languages and cultures. This includes any language variants such as French and French Canadian.

It is usually in the final stage of a translation or a localization project and endeavours to find and correct all the mistakes, mistranslations and style guide irregularities that may have been overlooked at earlier stages. LQA is an often overlooked project phase but it can make the difference in turning your translated content from good to excellent.

What is the Purpose of an LQA Review?

Adding a linguistic quality assurance review stage to your translation projects ensures the highest quality in your final products.

Some believe that editing and proofreading are enough to catch the majority of errors produced within the translation process. While they do help detect mistakes, some will often slip through undetected. This is often due to a difference of opinion on subjective translation errors and the lack of a standardised ruleset. That is why fixed guidelines on translation criteria are an essential part of a quality assurance system.

How does Quality Assurance differ from Quality Assessment?

Quality assurance and quality assessment are related but not the same. Typically quality assurance (QA) refers to the system or process that helps optimise the quality as a whole across a project, such as the translation of an app, a product or a large document.

A quality assessment is a step added to evaluate the quality of a translation against a pre-defined set of standards. For example, a quality assessment can be performed by a third-party linguist who evaluates and scores the translation for completeness and correctness.

After a translation is completed, another additional step, the review, is added to ensure the overall linguistic quality of a project. While a review can also look at individual pieces of translated content, for example checking only some specific parts of the translation, it also allows you to take a step back and look at the final product as a whole.

The review should be carried out by language experts who have not been involved in the process so far, to provide the greatest level of objectivity. This will ensure the content achieves the highest standard of linguistic quality and cultural consistency.

In addition to the above, there is also Localization Quality Assurance to consider. This looks at the bigger picture beyond simply the translated content and makes sure the necessary tools are in place to assess the layout, formatting, imagery and even UX functionality of content such as websites, video games, documents or software applications. It can also be used to assess the entire process end to end and find workflow improvements.

However, all of these systems and processes are working towards the same goal: providing high-quality translations and localized content for your audiences.

Linguistic Quality Assurance: An Essential Workflow Step

Certain industries, where translation quality can result in legal ramifications or even potential harm to an end user, cannot afford to not include quality assurance in their translation workflows.

Poor quality translations will reduce a company’s credibility and reputation and can often prove to be a costly mistake to fix. They can delay or even prevent a product’s access to the global markets resulting in lost business going to your competitors.

At LanguageWire we hold quality at the centre of everything we do for our clients and within our internal processes. You can learn more about our commitment to our quality policy here.

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

Thank you for your cooperation and welcome to 2024!

28.12.2023 by Katja Virtanen