Neural Machine Translation Post-Editing (NMTPE) ignores your knowledge base and preferences, and translators hate it. Our Context Sensitive (CS) Translate improves productivity, incorporates your knowledge base, and gets more positive feedback from translators.
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We analyzed 4.3 million translated segments to measure how our Context Sensitive approach compares to NMT. What we found was a productivity increase of 22.08%. Check out our research here and see for yourself!
Our Context Sensitive Translate is like a smart blender for translations, combining Translation Memories, Machine Translations, and Glossaries into a single, intelligent feed that learns with you and from you.
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Translators don't need to make the same changes repeatedly. Every segment interaction informs the next one with previous suggestions in terms of terms, tone, and more. This means the translator is shaping the engine's output as they go.
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Context Sensitive Translation isn't our only augmented feature. We also have "Proofread," "Smells," "Fix Tags," and more. What are all those? Scroll down to find out.
Proofread scans your translation and suggests fixes to any potential mistakes based on contextual analysis.
Translation Smells is a semantic analyzer. It examines a given translation looking for potential deviations in meaning, omissions and a wide variety of mistakes. While the absence of smells does not mean a translation is error-free, the presence of smells is a probable indication of errors worth examining.
The Alternative Suggestion provides another way to express the same translation. In addition, it also explains the rationale behind the alternative suggestion. This feature is perfect for when you feel like the best translation is right on the tip of your tongue.
Our Review Assessment categorizes the nature of the edits performed to a translation segment and describes how the change makes the edited version better or worse than the original translation.
Learning Terms extracts terms and their respective translations as a part of the translation product. Terms are added as learning terms and can later be upgraded to full glossary terms by users with the right level of permissions.
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Our Part of Speech Classifier identifies the correct part of speech for any term added to your terminology.
Fix Tags makes sure the translated text preserves the same tag structure as the original source text.
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There is never enough focus on cyber-security. Our SOC2 Type II certification is a small token that conveys our commitment to information security best practices. From encryption to MFA and data storage to transfer, we have security as a key focus.
We support dozens of file types with a unique file parsing and segmentation approach. Everything from complex structured files such as XML and YAML to simple Microsoft Word and PowerPoint files are supported. Our platform makes it easy for translators to focus on their translations and prevents them from making undesirable changes to code, variables, and file structures.
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