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        <title>Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation</title>
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            <title>Proceedings of the Fifth Conference on Machine Translation</title>
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    <abstract>In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.</abstract>
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%0 Conference Proceedings
%T Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation
%A Lee, Dongjun
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lee-2020-two
%X In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.
%U https://aclanthology.org/2020.wmt-1.118
%P 1024-1028

Markdown (Informal)

[Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation](https://aclanthology.org/2020.wmt-1.118) (Lee, WMT 2020)

• Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation (Lee, WMT 2020)

ACL

• Dongjun Lee. 2020. Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation. In Proceedings of the Fifth Conference on Machine Translation, pages 1024–1028, Online. Association for Computational Linguistics.