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Unified Prompt Learning Makes Pre-Trained Language Models Better Few-Shot Learners.

, , and . ICASSP, page 1-5. IEEE, (2023)

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Adapter Tuning With Task-Aware Attention Mechanism., , and . ICASSP, page 1-5. IEEE, (2023)Select the Best Translation from Different Systems Without Reference., and . NLPCC (1), volume 11838 of Lecture Notes in Computer Science, page 355-366. Springer, (2019)Learning Trajectory Patterns via Canonical Correlation Analysis., and . Int. J. Cogn. Informatics Nat. Intell., 15 (2): 1-17 (2021)Instance-aware Prompt Learning for Language Understanding and Generation., , , and . CoRR, (2022)Exploiting Curriculum Learning in Unsupervised Neural Machine Translation., and . EMNLP (Findings), page 924-934. Association for Computational Linguistics, (2021)Improving Unsupervised Neural Machine Translation via Training Data Self-Correction., and . LREC/COLING, page 8942-8954. ELRA and ICCL, (2024)BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing., , , , , , , and . CoRR, (2023)Instance-Aware Prompt Learning for Language Understanding and Generation., , , and . ACM Trans. Asian Low Resour. Lang. Inf. Process., 22 (7): 199:1-199:18 (2023)Unified Prompt Learning Makes Pre-Trained Language Models Better Few-Shot Learners., , and . ICASSP, page 1-5. IEEE, (2023)CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark., , , , , , , , , and 25 other author(s). CoRR, (2021)