CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
This work addresses translation quality improvement for machine translation users, but it is incremental as it applies existing methods to new tasks.
The authors tackled the tasks of Automatic Post-Editing and Multimodal Machine Translation by building systems using neural sequence-to-sequence learning methods, achieving competitive results with statistical phrase-based systems.
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.