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        <title>Cross-Lingual Transformers for Neural Automatic Post-Editing</title>
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    <abstract>In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Transformers for Neural Automatic Post-Editing
%A Lee, Dongjun
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F lee-2020-cross
%X In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.
%U https://aclanthology.org/2020.wmt-1.81
%P 772-776

Markdown (Informal)

[Cross-Lingual Transformers for Neural Automatic Post-Editing](https://aclanthology.org/2020.wmt-1.81) (Lee, WMT 2020)

• Cross-Lingual Transformers for Neural Automatic Post-Editing (Lee, WMT 2020)

ACL

• Dongjun Lee. 2020. Cross-Lingual Transformers for Neural Automatic Post-Editing. In Proceedings of the Fifth Conference on Machine Translation, pages 772–776, Online. Association for Computational Linguistics.