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Key Components |
PROMT Dictionaries PROMT Translation algorithms |
Translation Model Language Model |
PROMT Baseline Rule-based Engine Language Model Statistical post-editing Additional dictionaries |
Benefits |
Full control over terminology and translation style More accurate syntax and morphology Predictable and deterministic Profiling (multiple profiles can be easily created in one engine) |
Fast and fully automated engine training (in most cases, language independent) More fluent and “human-like” MT output |
More customizable and predictable than pure SMT More fluent and “human-like” MT output than pure RBMT Engine training is faster than pure RBMT Profiling (multiple profiles can be easily created in one engine) |
Limitations |
Language-dependent (algorithms depend on source/target languages) High customization effort |
Requires large and clean parallel corpora for training Domain-specific (usually trained on/for specific texts) Hard to customize the translation of a particular word/construction |
Requires a parallel corpora for training (but less than pure SMT) Domain-specific (usually trained on/for specific texts) |
Available Languages |
English, Russian, German, French, Spanish, Italian, Portuguese, Chinese (Simplified and Traditional), Ukrainian, Kazakh, Turkish, Bulgarian, Latvian, and Polish |
Any language pair, for which there are enough training data |
English, Russian, German, French, Spanish, Italian, Portuguese |
Available Products |
Desktop and Server solution |
Server-based solutions only |
Server-based solutions only |
Prerequisites for Engine Training |
Source texts (at least, 100,000 words / 10,000 translation units) |
Parallel corpora (at least, 5,000,000 words / 500,000 translation units) |
Baseline rule-based profile (if there is no custom rule-based profile, PROMT uses an out-of-the-box profile) Parallel corpora (at least, 500,000 words / 50,000 translation units) |
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