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Vega-MT: The JD Explore Academy Translation System for WMT22., , , , , , , , , and 2 other author(s). CoRR, (2022)Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning., , , , , and . CoRR, (2024)Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation., , , , , and . CoRR, (2022)Prompt-Learning for Cross-Lingual Relation Extraction., , , , , , , and . IJCNN, page 1-9. IEEE, (2023)Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation., , , , , , and . CoRR, (2023)Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training., , , , , and . ACL (Findings), page 10932-10940. Association for Computational Linguistics, (2023)Learn from Object Counting: Crowd Counting with Meta-learning., , , , and . IET Image Process., 15 (14): 3543-3550 (2021)Vega-MT: The JD Explore Academy Machine Translation System for WMT22., , , , , , , , , and 2 other author(s). WMT, page 411-422. Association for Computational Linguistics, (2022)On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation., , , , , and . COLING, page 5029-5034. International Committee on Computational Linguistics, (2022)