Mastering Machine Translation Quality in 2024: The Ultimate Guide
Machine translation, AI, ChatGPT – it seems like technology is dominating the realm of translation and localization in the 21st century. But as we delve deeper, we begin to understand that without proper guidelines and quality criteria, our automated localization efforts might result in a disaster. Let’s take a look at how to manage machine translation quality and how you can implement it into your translation management workflow.
Every day, a staggering 100 billion words are processed by Google Translate alone, the equivalent of translating Tolkien’s entire Lord of the Rings trilogy over 170,000 times.
Priority for most businesses is to have high volumes translated at the snap of a finger. With the tools we have at our disposal today, obtaining instant machine translation output is a piece of cake.
At first, it seems you’ve solved your problem immediately. But the more you think about it, the more questions pop up.
How can you tell the output is accurate?
How do you assess the quality?Can you use it right away, or do you need a professional to edit it?
Light post-editing or full?
And so on, and so on…
It can pose a challenge, especially in today’s landscape with the growing prominence of AI-powered tools. It’s increasingly harder to tell whether you can trust machine translation, and to what extent you should review it. Luckily, we at Translata have not been idle and kept looking ahead.
The result is this practical guide – to help you master the quality of machine translation in a fast-paced world.
It will be your North Star in the daunting yet rewarding sea of AI and latest tech developments.
With practical tips from leaders in the industry, this journey promises realistic implementation techniques, algorithm breakdowns, and a sneak peek into technologies yet to boom.
Let’s take on this ever-shifting environment together.
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Understanding Machine Translation Quality Assessment (MTQA)
Machine Translation Quality Assessment, or MTQA, is the process to determine the quality of MT output based on several parameters, like:
Along with human review, we can ensure that the machine translation communicates the intended message effectively and without distortions or misinterpretations.
Why is MTQA important?
MTQA – if done right and with the help of professionals – can optimise your cross-cultural communication and ensure more efficient translation management. It can lead to more accurate outputs, saving you time and money in the process.
You can avoid costly mistakes or potential damage to your reputation by integrating MTQA workflow in your translation management process. Making sure that the crucial subtleties of language, context, and intent are preserved is key to consistent, high-quality translations.
How to perform MTQA?
Ideally, with the help of a seasoned translation provider with extensive knowledge and experience.
First, you need to look at the text and determine:
Is it a critical business dialogue?
Then you need to find a method that will best suit your needs, budget, and field of expertise.
Different types of texts will require different levels of accuracy and evaluation criteria. Knowing how to choose a suitable evaluation method will help you achieve efficient and more reliable communication.
There are 3 categories of MTQA: automated, human, and hybrid.
Automated evaluation metrics are algorithms that compare machine translation output with human reference translations. They serve as a quick and cost-effective way to assess the quality.
Automated evaluation includes methods such as BLEU (Bilingual Evaluation Understudy), TER (Translation Edit Rate), and METEOR (Metric for Evaluation of Translation with Explicit Ordering). These metrics provide useful insights, but it’s essential to remember their limitations – they largely focus on the surface form of texts rather than their underlying meaning.
The Bilingual Evaluation Understudy (BLEU) score is the most popular automated metric. It measures the overlap of the MT output and the reference translation. A higher BLEU score indicates a closer match to the reference translation and a better (supposedly) translation quality.
The drawbacks of this method lie in its limited flexibility – the model penalizes everything that doesn’t correspond to the exact wording of the reference translation, so the results should be taken with a grain of salt.
TER and WER
Translation Edit Rate (TER) and Word Error Rate (WER) are automated evaluation metrics that involve different methods. TER simply counts the number of edits needed to reach the reference translation while WER looks at the proportion of incorrectly translated words.
Both are great if you’re looking to quantify the mistakes. But neither considers whether the mistakes are minor, major, or critical. This could distort the final score.
Assessment or evaluation by a human professional is the costliest AND the most accurate way to determine machine translation quality. It’s the “golden standard” in the industry – valuable feedback from translators, reviewers, or end-users can provide insights that algorithms may miss.
There are two sub-categories that help us determine the MT output quality – fluency & adequacy and error analysis.
Fluency and Adequacy
Human MTQA looks at two key aspects – adequacy and fluency. Adequacy gauges the degree to which the MT output conveys the meaning of the original. Fluency speaks to how natural the machine output sounds and whether it’s mistake-free.
Analysing errors helps us gain insights and better determine the quality and suitability of MT engines. Basic error categories distinguish grammatical, semantic, and lexical. Identifying these types of errors can serve as a guide for future improvements of the MT engine.
What’s the solution?
In order to harness the full potential of your MT projects, a balanced and hybrid approach to MTQA is the way forward.
Combining automated evaluation with insightful human review will set the groundwork for consistently more reliable, high quality machine translations.
Improving Machine Translation Quality
The Role of Pre-Editing
You’ve probably heard of post-editing. The process of editing the MT output to suit the original. But to achieve the highest quality output, pre-editing is essential.
It mainly involves the simplification and clarification of the source text, enabling machines to interpret and translate the text more effectively. Localization engineers are the ones responsible for the initial ‘text architecture’ which results in more efficient translation workflow.
Just think about all the times you’ve sent your LSP a non-editable PDF. To achieve efficient workflow and the best possible output for the linguist, the PDF needs to be converted and then pre-edited.
In some instances, localization engineers may go even further and pre-edit Word formats. The process involves simplifying complex sentence structures, removing ambiguities and ensuring terminological consistency. Stripping your sentences down to their simplest form takes out the guesswork for the machine, increasing the chances of obtaining higher quality MT output.
Pre-editing is essential because it can dramatically impact the overall quality of the translation and streamline the process. A concise, clear and straightforward source text often results in a more precise and coherent translation, minimising the need for post-editing work.
Less work for the post-editors translates into faster delivery and higher customer satisfaction.
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The Impact of Post-Editing
Machine translations aren’t perfect, and professional human post-editing is crucial in achieving the desired quality. Above all else, it involves:
- correcting translation errors,
- refining the tone and style and
- ensuring the translated content conveys the same meaning as the original text.
Errors or mistranslations that the raw machine translation overlooks (or creates) are corrected, as the translators chisel away to create an accurate and natural-sounding text. The role of human post-editors caters to situations where the machine might misinterpret literal meanings, or fail to accurately incorporate the cultural and contextual nuances.
While translation technology continues to advance, human intervention in the post-editing stage is vital not just to refine translations and help avoid misunderstandings. It’s also crucial for LSPs and you, their clients, as the refined machine outputs stored in translation memories save time, money, and other valuable resources.
You can learn more about translation tech and how you can benefit from the latest developments in our comprehensive guide to translation technologies.
Next time you find yourself in dire straits – with a 3,000-word legal contract and same day delivery, machine translation and our skilled post-editors just might save you.
Choosing the Right Machine Translation Engine
You’ve probably heard of them, or even used them. Machine translation engines such as DeepL, Google Translate, or our online translator are freely available. But we in Translata know that not all engines are created equal. How they are built and what they are optimised for can widely differ.
Some engines might be superior in translating certain languages, while others may triumph in specific industries or content types. That’s where we come in – understanding your unique requirements and aligning them with the strength of a specific machine translation engine is crucial.
Don’t just shoot in the dark hoping for the best. Ask for tailored advice and let us help you navigate the landscape. We will take complex care of the workflow, making sure the engine aligns with the specified language pair, your content type, and quality expectations.
IV. The Road Ahead
Leveraging Technology to Improve Machine Translation Quality
Machine Translation Quality Prediction – Your Roadmap to Success
Machine translation quality prediction is one of the clearest paths forward. But what exactly is it? It involves the use of algorithms and AI to forecast the quality of translated output even before human post-editing. It highlights the most problematic areas, and helps post-editors focus on the crucial parts. It’s a part of the larger machinery called translation management, set to revolutionize the field come 2024.
Think of machine translation quality prediction as an investment. One that yields a handsome ROI while providing an unparalleled edge. By identifying potential issues upfront, you can achieve a more efficient workflow, freeing translators’ time and capacities. You’re delivering error-free, consistent outputs, driving a more engaging and seamless user experience across languages and markets.
Strategically combining these technologies with the expertise and experience of a reliable partner can help your company overcome linguistic barriers efficiently and successfully navigate the global landscape.
The Final Decode on Mastering Machine Translation Quality
Machine translation quality in 2024 is all about the combination of human expertise, technology, and constant fine-tuning. It’s a delicate balance, but once found, it unlocks a new world of possibilities for global communication.
The insights shared today are your roadmap to mastering this craft. It’s not just about the right tools. It’s about understanding linguistic nuance, localising context, and continuous improvement through feedback loops.
But why does it matter? Because in an interconnected world, high translation quality unlocks wider audience reach. It's your business's passport to seamless, boundary-defying communication.
In Translata, we’ll help you analyse your current translation management. Identify gaps. Strategise on integrating technology, human insight, and continuous improvement. Note the role of translation memories and termbases. Save time, increase accuracy, and reduce costs.
Wondering where to start? Give us a call or shoot us an e-mail – our Sales and Project teams are always happy and ready to help.