Abstract
SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute 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.- Anthology ID:
- 2020.eamt-1.66
- Volume:
- Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Lisboa, Portugal
- Editors:
- André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 491–492
- Language:
- URL:
- https://aclanthology.org/2020.eamt-1.66
- DOI:
- Cite (ACL):
- Reinhard Rapp and George Tambouratzis. 2020. An Overview of the SEBAMAT Project. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 491–492, Lisboa, Portugal. European Association for Machine Translation.
- Cite (Informal):
- An Overview of the SEBAMAT Project (Rapp & Tambouratzis, EAMT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.eamt-1.66.pdf