Project - SEBAMAT Semantics-Based Machine Translation
PROFILE

SEBAMAT Semantics-Based Machine Translation

Start date: 04-01-2020
End date: 31-03-2022
Funded by: H2020-MSCA-IF-2018 (Marie Skłodowska-Curie Individual Fellowships)
Project leader: George Tambouratzis
 
SEBAMAT (semantics-based MT) is a Marie Curie project intended to contribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.