  This paper describes multimodal machine translation systems developed jointly
by Oregon State University and Baidu Research for WMT 2018 Shared Task on
multimodal translation. In this paper, we introduce a simple approach to
incorporate image information by feeding image features to the decoder side. We
also explore different sequence level training methods including scheduled
sampling and reinforcement learning which lead to substantial improvements. Our
systems ensemble several models using different architectures and training
methods and achieve the best performance for three subtasks: En-De and En-Cs in
task 1 and (En+De+Fr)-Cs task 1B.
