translation agency

Why accuracy is critical to medical translations

16.4.2024 by Delingua

In medical translations, there can be no room for error. A wrong word can sometimes even mean the difference between life and death.

In the tragic case of Willie Ramirez from South Florida, USA, it was the misunderstanding of one word, ‘intoxicado’, that caused his treatable brain bleed to be missed with devastating consequences.

In Cuban Spanish, ‘intoxicado’ signifies an illness brought on by something you have eaten or drunk. Willie wrongly assumed that his intense headache was being caused by something he had consumed at a restaurant earlier that day. This was misinterpreted by the hospital staff as a drug overdose, and he was treated as such. By the time they realised their mistake, it was too late.

The results tragically left Willie a quadriplegic for life. The hospital was sued for $71 million dollars, and Willie’s mother, who took her son to the hospital, has chastised herself ever since for using the ‘wrong word’.

What are medical translations?

This example highlights the risks of not having professional translators available in medical facilities. But within the sphere of life sciences, there are lots of types of translation that require professional medical translation services.

Interpreting

In health centres, hospitals or doctor’s offices, non-native speakers may need a professional hospital interpreter to help them understand what critical information the doctor is relaying and make the patient fully understood.

Labelling and inserts

Pharmaceutical manufacturing companies need to ensure that the right information is printed on their labels, including dosage, instructions for usage, and side effects. Medical devices and surgical equipment would also fall into this category as they need to be properly labelled to avoid malpractice.

Scientific and technical translation

Research reports, studies, clinical trials and interpreted data all need to be translated by an expert medical translator who understands the terms and the data represented exactly in both languages.

Medical documentation

Any leaflets or information documentation, both online and in print, that is needed to keep the public well-informed must be accurately translated into numerous language variants. This includes any marketing or advertising materials for new drugs that must accurately represent the product.

Educational material

Scientists and medical professionals require continuous ongoing training to keep their skills in their prime. Educational textbooks and eLearning materials must be kept up to date and accurately translated to ensure the highest standard of training is maintained.

The consequences of inaccurate translations

Inaccurate medical translations could have wide-reaching consequences that heavily impact people’s lives.

Loss of life, permanent disability or impact on a patient’s lifestyle
Malpractice lawsuits and loss of licenses to practice or manufacture goods
Delays in the clinical trials that halt scientific progress
Reputation damage for companies and individuals

Expert translators can help both individuals and businesses avoid these serious consequences of medical mistranslations.

Work with a medical translations expert

For high-quality medical translations, you need to ensure that you work with a Language Service Provider who can team you up with the best experts in their respective fields. Experts not only with scientific expertise but also a deep understanding of the culture and the language.

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

Validation – high-quality translations are a joint effort

27.3.2024 by Jari Herrgård

Successful translations require teamwork. Although the key person in the translation process is the translator and their tools, they alone are not able to achieve ideal quality. In the translation process, the best possible result always requires input from the translation agency’s other employees as well as the customer. Validation is perhaps the most important tool for assessing the linguistic quality of translations.

If you are a customer, you may be asking why you should contribute to the process – it’s our job, after all. You are correct and, as a customer, your involvement is not obligatory. But if you want the best result, it is in your best interest to participate in the process – especially through feedback.

To some extent, language is a matter of opinion. We are all fluent in at least our own native tongue, and many of us also know several other languages. Therefore, we all have a lot of opinions about language. This is especially true in work contexts if language plays a central role in our work and we put a lot of thought into how a message should be conveyed. We know how we want to address the reader, and perhaps the organisation we work in has even defined common rules for what kinds of images we should evoke.

Even if these rules are self-explanatory within the organisation and come naturally to you, they may not be self-evident to the translator. The translator is always a bit of an outsider and depends on what they are told about your preferred language and its nuances. This is where teamwork comes into play: the ideal translation process involves cooperation to ensure that the translation matches the customer’s communication needs as closely as possible. By providing feedback on the translations, the customer can ensure that each translation is better than the one before and closer to the customer’s ideal.

Validation as a tool

One of the most important tools for the process described above is validation. This means a review by the customer after the translation has been completed. It may often be carried out by an expert whose main work tasks do not include translation. The expert is responsible for checking, in particular, the terminology and style, and assessing whether they are in line with the customer’s brand. Other review work can also be done at the same time.

Validation in itself is a simple step, and there is no doubt about its usefulness, but it is still often skipped altogether. Typically, validation is carried out by sending PDF or DOCX files to vendors or technical experts, for example. The expert will use the comment or track changes functions to add their own comments and corrections to the document. These will be included in the final version and imported into the translation memory by the translation agency. This process is quite laborious and may complicate version control, which are the main reasons why it is often skipped.

Validation with system support

At LanguageWire, workflow and translation work are based on a shared platform used by customers, translators and internal project managers. Using a single platform has many benefits, especially in terms of translation project communications and transparency.

The validation step also takes place entirely on this platform, which makes the process quick and efficient. Usually, the validation step is added as soon as the project is created.

Validation takes place after the translators have done their work. The experts assigned to the project will receive an automatic message from the system and, by clicking on the link, they will be forwarded to the right project. There is no need to search for the project or upload any files, and the expert can start working in their browser right away.

Validation is done in the same tool that the translators use, and the validator has access to all the same information. This means, for example, that they will automatically be able to view the correct terminology if there is a glossary attached. This helps to avoid situations where outdated or otherwise incorrect terminology is used in the text during the validation phase. The validator also has access to other tools, such as proofreading and translation tools. Our user-friendly interface ensures that you do not need to be a professional translator to benefit from the translation platform. Everything you need can be done with basic browser skills.

In addition to possible corrections, the validator can also communicate with the translator, which helps to quickly solve potential dilemmas. Since everyone works in the same digital environment, all corrections are also saved directly in the translation memory, and they do not need to be separately exported at the end of the project.

In order to ensure the best possible translation quality and that the translator and proofreader learn as much as possible, a language specialist will check the changes made by the validator at the end. This ensures that all changes follow the customer’s own standards and, of course, the general rules of the language.

Would you like to discuss your organisation’s language needs? Contact us via email at contact-fi@languagewire.com for further information!

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)

Captions vs Subtitles: What is the difference?

24.1.2024 by Delingua

The terms ‘caption’ and ‘subtitle’ are often used rather interchangeably. Each is a transcription of an audio or video recording that displays the spoken language as words on a screen.

This serves as an excellent tool to make recorded media more accessible to viewers in various circumstances. For example, it is well known that many people view online videos without sound, meaning they read the text on their device because they don’t want to disturb others nearby, or have physical hearing impairments.

In fact, “more than 100 empirical studies document that captioning a video improves comprehension of, attention to, and memory for the video. Captions are particularly beneficial for persons watching videos in their non-native language, for children and adults learning to read, and for persons who are D/deaf or hard of hearing.” (Gernsbacher, 2015)

By creating the transcription of the words spoken (and/or displayed on screen as graphics or titles) in their original language, this transcription can then be used to generate a localisation (translation) to appeal to many more audiences from different international markets.

The transcription could be generated as a single block of text, typical of written prose. But for the purpose of joining the text with certain images on a screen, it is necessary to generate coordinated time code. This is where a transcription becomes much more sophisticated, requiring professional software and services.

When we look more closely at the different potential consumers of video and audio content, there are subtle and important differences between captions and subtitles.

Similarities between captions and subtitles

Each is a text file accompanied by time code for synchronization to media. The most common format is SRT.

At a minimum, the text includes a transcription of what is being spoken. Text may also include on-screen text for subsequent translation (localization).

Differences between captions and subtitles

Subtitles generally only include a transcription of what is being spoken in the video or audio recording. These are often translated into various languages.

Captions can include other elements that describe sounds and music (for the hearing impaired) and can be toggled on and off (closed captions). Of course, captions can also be translated.

There are very many more detailed differences and options when configuring captions and subtitles, and many sources to learn about these differences.

Important considerations when ordering subtitling or captioning services

Is there existing on-screen text that needs to be included in the transcription for the purpose of translation?

Will the subtitles/captions cover up existing text (such as names and titles of speakers)? In this case, there may need to be another step involving specific placement of a caption on the screen.

Should the transcription include indications of other sounds (such as music) for hearing-impaired viewers?

Subtitling and captioning services are an excellent way to add to the value of your media content. It makes vastly more accessible to a much wider audience. It may also improve to increase the comprehension of viewers, even if they might otherwise understand the original language, as many people are more visual that aural learners.

This article was originally published by LanguageWire Company (John Jones)