project . 2020 - 2022 . On going

SEBAMAT

Semantics-Based Machine Translation
Open Access mandate for Publications and Research DataOpen Access mandate for ... European Commission
  • Funder: European CommissionProject code: 844951 Call for proposal: H2020-MSCA-IF-2018
  • Funded under: H2020 | MSCA-IF-EF-ST Overall Budget: 165,085 EURFunder Contribution: 165,085 EUR
  • Status: On going
  • Start Date
    01 Apr 2020
    End Date
    31 Mar 2022
  • Detailed project information (CORDIS)
Description
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 translation quality to the next level as long as the systems do not make better use of semantic knowledge. For example, according to Kevin Knight future machine translation syst...
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Description
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 translation quality to the next level as long as the systems do not make better use of semantic knowledge. For example, according to Kevin Knight future machine translation syst...
Partners
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