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The paper describes Tilde's EnglishLatvian and Latvian-English machine translation systems for the WMT 2017 shared task in news translation. Both constrained and unconstrained systems are described. Our constrained systems were ranked as the best performing systems according to the automatic evaluation results. The paper gives details to how we pre-processed training data, the NMT system architecture that we used for training the NMT models, the SMT systems and their usage in NMT-SMT hybrid system configurations.

This paper describes the submissions to the efficiency track for GPUs at the Workshop for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz University, Tilde and University of Alicante. We focus on efficient implementation of the recurrent deep-learning model as implemented in Amun, the fast inference engine for neural machine translation. We improve the performance with an efficient mini-batching algorithm, and by fusing the softmax operation with the k-best extraction algorithm. Submissions using Amun were first, second and third fastest in the GPU efficiency track.

The paper describes the development process of the Tilde's NMT systems that were submitted for the WMT 2018 shared task on news translation. We describe the data filtering and pre-processing workflows, the NMT system training architectures, and automatic evaluation results. For the WMT 2018 shared task, we submitted seven systems (both constrained and unconstrained) for English-Estonian and Estonian-English translation directions. The submitted systems were trained using Transformer models.

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