CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
Charles University in Prague（布拉格查理大学）
英文摘要：
Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
