Abstract
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.
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CITATION STYLE
Libovický, J., Helcl, J., Tlustý, M., Bojar, O., & Pecina, P. (2016). CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 646–654). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-2361
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