Tilde at WMT 2020: News Task Systems

• URL: http://arxiv.org/abs/2010.15423v1
• Date: Thu, 29 Oct 2020 08:59:37 GMT
• Title: Tilde at WMT 2020: News Task Systems
• Authors: Rihards Kri\v{s}lauks, M\=arcis Pinnis
• Abstract summary: This paper describes Tilde's submission to the WMT 2020 shared task on news translation for both directions of the English-Polish language pair. We build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models. Our final models are ensembles of Transformer base and Transformer big models that feature right-to-left re-ranking.
• Score: 0.38073142980733
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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