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AbstractThis paper presents the team TransQuest's participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.
CitationRanasinghe, T., Orasan, C. and Mitkov, R. (2020) TransQuest at WMT2020: Sentence-Level direct assessment. In Proceedings of the Fifth Conference on Machine Translation, pp. 1049–1055, Online. Association for Computational Linguistics..
JournalFifth Conference on Machine Translation
Description© 2020 ACL. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2020.wmt-1.122
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/