Unedited output from the MT engine, suitable for low-impact content with the fastest turnaround time. This can be done with trained or untrained engines.
In the modern world, businesses require fast and effective translation solutions to overcome language barriers and connect with a larger audience. Machine Translation (MT) is a valuable tool that utilizes sophisticated algorithms and artificial intelligence to deliver rapid and accurate translations across various content types and languages.
For critical content like legal documents, medical records, or technical manuals, Human Translation (HT) is preferred for its accuracy. Skilled experts ensure precision. Machine Translation Post-Editing (MTPE) uses software initially, refined by experts. Choose the right method to maintain quality, respecting cultural nuances and your brand's voice. Opt for Human Translation for sensitive or complex content, involving experts at every step.
Although MT offers many advantages, it also has its limitations. The lack of human touch present in raw MT output can result in translations that may not accurately capture the subtleties of language, cultural nuances or client preferences. To overcome these limitations, Akorbi offers different levels of MT output.
Unedited output from the MT engine, suitable for low-impact content with the fastest turnaround time. This can be done with trained or untrained engines.
Often use when the main focus is to improve the readability and objective accuracy of a text with minimal human intervention, making it an ideal approach for projects that prioritize efficiency and quality.
Comprehensive human review to refine the translation, ensuring it is of the highest quality and culturally appropriate.
Akorbi perform a comprehensive rollout process including a personalized discovery phase for your content, language and quality needs. This may result in proposing Engine Training before kicking off MT in production. MT engine training will help customize the base model to industry and client – specific language and style. This in turn reduces the amount of edits necessary to be made by the post-editor, and thereby time and cost involved in the process, compared to that of an untrained engine.
This approach is particularly valuable for specialized industries, where standardized terminology is essential for effective communication. For engine training, it is important to have large client data in the form of translation memories and glossaries, however even more critical is the quality of this data. The MT engine will directly reflect the data it is trained on, in its output, so benefits will be reaped by maintaining clean data for this process.
It is important to note that training an engine does not re-train the MT base model engine, but acts as an overriding mechanism that applies predetermined translations for specific terms and general style preferences across the text. This ensures that custom terminology and style is prioritized while still benefiting from the extensive training of the underlying language models.
The BLEU (Bilingual Evaluation Understudy) score is a metric that evaluates the quality of machine-translated text by comparing it to reference translations. By continually analyzing the performance of various MT engines for different languages and content types, and selecting the most suitable engine based on these findings, the highest translation quality can be achieved.
The evaluation process involves two steps: automated metrics and human evaluation. The first step involves the use of metrics like the BLEU score, and soon, others like METEOR, COMET, and TER. The second and most valuable metric is human evaluation, which incorporates the Post-Edit Distance (PED) metric. This metric is used on an ongoing basis to accurately measure turnaround and pricing expectations, as well as to monitor quality levels by identifying project and language trends. Through this combination of automated and human evaluation, the overall translation quality can be consistently maintained and improved.
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