Case studies Archives

AI QE Works — But Still Needs a Human Brain: What Custom.MT QE Benchmark Reveals

Independent MTQE Benchmark Findings Across 17 Language Pairs DOWNLOADS: Presentation, Calculate your QE threshold As companies move large volumes of content through machine translation, the next strategic question becomes unavoidable: Can you let AI (not humans) decide which sentences are good enough to publish? This is the promise of Quality Estimation (QE): a reliable, automated […]

Deepseek for Translation 2: a Robin Hood Moment in AI

Part 1 of the evaluation with scores Full-sized infographic Сommunity’s evaluation of Deepseek’s translation accuracy is complete. Conclusion: Deepseek is a top-notch translation powerhouse racing nose-to-nose with the market leaders. The differences were small, and the ratings depended more on personal preferences of evaluators towards computer-generated translation, rather than model performance. And it will get […]

Deepseek for Translation: a Flash Evaluation

On January 28 we called for language industry peers to evaluate the new large language model Deepseek for translation tasks. More than 30 volunteers came forward, and the first results have already come in. We’re hoping to have at least 10-15 language combinations evaluated within the scope of this expercise. We will publish information as […]

Custom.MT Brings ChatGPT to Trados Users

Custom.MT integrated ChatGPT and made it available through a Trados Plugin. Try it, the first 50,000 characters are on us. Like all technology teams out there, Custom.MT is experiencing the AI revolution with awe and excitement. As soon as OpenAI made waves, we decided to make it work for localization people. Working on our first […]

Tools for Data Labeling in Machine Translation Evaluations

Running professional human evaluations of machine translation requires detailed methodology and software tools to streamline the process. The most detailed approach to evaluation today is to label errors and assign weights to critical issues using a variation of DQF/MQM ontology. In this article, we outline Custom.MT’s journey to selecting and implementing a tool for human […]

Subtitling with Trained Machine Translation: the 4 Next Steps

Author: Konstantin Dranch Subtitlers are looking to adopt customized machine translation, but video localization systems are not ready for it yet. Here is what they could do to expedite up the process. Since Google introduced AutoML translation and Microsoft created Custom Translator, text translation management systems (TMS) have developed excellent support for trainable machine translation. […]

French to English Finance Machine Translation Beats Human

Case study In this case study, we look at a machine translation engine training project with a French LSP. We trained a set of MT engines and evaluated the performance both automatically and with a human eye with the client’s pool of linguists. Language combination: French to English Domain: Financial documentation Training dataset: 607k parallel […]

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