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        <title>The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional
%A Gwinnup, Jeremy
%A Sandvick, Joshua
%A Hutt, Michael
%A Erdmann, Grant
%A Duselis, John
%A Davis, James
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F gwinnup-etal-2018-afrl-ohio
%X 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.
%R 10.18653/v1/W18-6440
%U https://aclanthology.org/W18-6440
%U https://doi.org/10.18653/v1/W18-6440
%P 612-615

Markdown (Informal)

[The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional](https://aclanthology.org/W18-6440) (Gwinnup et al., 2018)

• The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional (Gwinnup et al., 2018)

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.