The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional

Jeremy Gwinnup, Joshua Sandvick, Michael Hutt, Grant Erdmann, John Duselis, James Davis


Abstract
AFRL-Ohio State extends its usage of visual domain-driven machine translation for use as a peer with traditional machine translation systems. As a peer, it is enveloped into a system combination of neural and statistical MT systems to present a composite translation.
Anthology ID:
W18-6440
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
612–615
Language:
URL:
https://www.aclweb.org/anthology/W18-6440
DOI:
10.18653/v1/W18-6440
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W18-6440.pdf