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Small Language Models Improve Giants by Rewriting Their Outputs.

, , , , , and . EACL (1), page 2703-2718. Association for Computational Linguistics, (2024)

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Small Language Models Improve Giants by Rewriting Their Outputs., , , , , and . CoRR, (2023)Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric., , , and . WMT, page 118-128. Association for Computational Linguistics, (2022)Subword Mapping and Anchoring across Languages., and . EMNLP (Findings), page 2633-2647. Association for Computational Linguistics, (2021)Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation., and . ACL (1), page 12087-12105. Association for Computational Linguistics, (2024)The IICT-Yverdon System for the WMT 2021 Unsupervised MT and Very Low Resource Supervised MT Task., , , , , and . WMT@EMNLP, page 973-981. Association for Computational Linguistics, (2021)Small Language Models Improve Giants by Rewriting Their Outputs., , , , , and . EACL (1), page 2703-2718. Association for Computational Linguistics, (2024)Active Learning by Acquiring Contrastive Examples., , , and . EMNLP (1), page 650-663. Association for Computational Linguistics, (2021)Domain Adversarial Fine-Tuning as an Effective Regularizer., , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 3103-3112. Association for Computational Linguistics, (2020)