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ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning.

, , , , , , and . SemEval@ACL/IJCNLP, page 810-819. Association for Computational Linguistics, (2021)

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When Do Program-of-Thought Works for Reasoning?, , , , , and . AAAI, page 17691-17699. AAAI Press, (2024)On Robustness and Bias Analysis of BERT-Based Relation Extraction., , , , , , and . CCKS, volume 1466 of Communications in Computer and Information Science, page 43-59. Springer, (2021)LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting., , , , , , , , and . COLING, page 2374-2387. International Committee on Computational Linguistics, (2022)Towards A Unified View of Answer Calibration for Multi-Step Reasoning., , , and . CoRR, (2023)From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal., , , , and . ICCV, page 12063-12073. IEEE, (2023)ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning., , , , , , and . SemEval@ACL/IJCNLP, page 810-819. Association for Computational Linguistics, (2021)Ontology-enhanced Prompt-tuning for Few-shot Learning., , , , , , , and . WWW, page 778-787. ACM, (2022)Disentangled Contrastive Learning for Learning Robust Textual Representations., , , , , , and . CICAI (2), volume 13070 of Lecture Notes in Computer Science, page 215-226. Springer, (2021)Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction., , , , , , , , and . NAACL-HLT (Findings), page 1607-1618. Association for Computational Linguistics, (2022)Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction., , , , , , , , and . IJCKG, page 115-120. ACM, (2021)