Have you noticed how no online MT tool (I tried online Systran, Google and Babelfish) is capable of translating transliterated Greek into English?
Given how popular transliteration is for languages with their own "exotic" alphabets, I believe this is an avenue worthy of further exploration.
Friday, January 2, 2009
Machine Translation or CAT?
Asking whether machine translation (MT) or computer-aided translation (CAT) works better is pretty much a version of the chicken or the egg question in the translation circles.
In Machine Translation, (usually rule-based) software translates text from one language to another, and the human translator acts as an editor who corrects and/or customizes the process to meet specific project/data/customer requirements.
CAT tools work like a dictionary or taxonomy of sorts that save human-generated translations and keep them easily accessible, organized and consistent.
CAT-Translated text segments are stored in special files called Translation Memories (TM), which are then used as a basis for new translations.
The former relies on NLP and corpus linguistics algorithms and heuristics for the translation of text from one language to another. The latter relies on human translation and capitalizes on storage and editing post-processes.
Both methods have their advantages and disadvantages.
In fact I believe that an "ideal" solution would involve a successful merge of both methodologies. MT methods seem to scale better and CAT methods seem to fare better in terms of precision.
In Machine Translation, (usually rule-based) software translates text from one language to another, and the human translator acts as an editor who corrects and/or customizes the process to meet specific project/data/customer requirements.
CAT tools work like a dictionary or taxonomy of sorts that save human-generated translations and keep them easily accessible, organized and consistent.
CAT-Translated text segments are stored in special files called Translation Memories (TM), which are then used as a basis for new translations.
The former relies on NLP and corpus linguistics algorithms and heuristics for the translation of text from one language to another. The latter relies on human translation and capitalizes on storage and editing post-processes.
Both methods have their advantages and disadvantages.
In fact I believe that an "ideal" solution would involve a successful merge of both methodologies. MT methods seem to scale better and CAT methods seem to fare better in terms of precision.
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