Tilde at WMT 2020: News Task Systems
Rihards Kri\v{s}lauks, M\=arcis Pinnis

TL;DR
This paper details Tilde's submission to the WMT2020 news translation task, focusing on English-Polish translation using Transformer models, data selection, back-translation, and ensemble techniques.
Contribution
It introduces improved translation systems with data selection and back-translation strategies, and ensemble methods for enhanced performance in news translation.
Findings
Ensembles of Transformer models improve translation quality.
Data selection and back-translation enhance system performance.
Right-to-left re-ranking contributes to better results.
Abstract
This paper describes Tilde's submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions from the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models that feature right-to-left re-ranking.
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Taxonomy
MethodsLinear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Attention Is All You Need · Residual Connection · Multi-Head Attention · Layer Normalization · Byte Pair Encoding · Adam · Softmax
