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This project focuses on improving machine translation by integrating semantic knowledge into the translation process. It proposes three innovative approaches that utilize word sense disambiguation, semantic role labeling, and semantic similarity to enhance translation accuracy and fluency.
Most current machine translation systems are either rule-based or corpus-based.
They typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries.
This is also true for the recent neural machine translation systems, which - in comparison to standard phrase-based systems, tend to have the focus even more on fluency rather than adequacy.
However, it has been pointed out that it is unlikely to be able to bring machine transla…
ATHENA - RESEARCH AND INNOVATION CENTER
ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS
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