Discovering Machine Translation Strategies Beyond Word-for-Word Translation: a Laboratory Assignment

نویسندگان

  • Juan Antonio Pérez-Ortiz
  • Mikel L. Forcada
چکیده

It is a common mispreconception to say that machine translation programs translate word-for-word, but real systems follow strategies which are much more complex. This paper proposes a laboratory assignment to study the way in which some commercial machine translation programs translate whole sentences and how the translation differs from a word-for-word translation. Students are expected to infer some of these extra strategies by observing the outcome of real systems when translating a set of sentences designed on purpose. The assignment also makes students aware of the difficulty of constructing such programs while bringing some technological light into the apparent “magic” of machine translation.

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تاریخ انتشار 2001