Title | ||
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Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation. |
Abstract | ||
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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. |
Year | Venue | DocType |
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2020 | WMT@EMNLP | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 |
Name | Order | Citations | PageRank |
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Dongjun Lee | 1 | 0 | 2.37 |