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
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 612–615
- Language:
- URL:
- https://aclanthology.org/W18-6440
- DOI:
- 10.18653/v1/W18-6440
- Cite (ACL):
- Jeremy Gwinnup, Joshua Sandvick, Michael Hutt, Grant Erdmann, John Duselis, and James Davis. 2018. The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 612–615, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional (Gwinnup et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6440.pdf