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COPEN: Probing Conceptual Knowledge in Pre-trained Language Models.

, , , , , , , and . EMNLP, page 5015-5035. Association for Computational Linguistics, (2022)

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GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation., , , , , , , , , and 1 other author(s). CIKM, page 5391-5395. ACM, (2023)Adversarial Multi-lingual Neural Relation Extraction., , , , and . COLING, page 1156-1166. Association for Computational Linguistics, (2018)Emergent Modularity in Pre-trained Transformers., , , , , , , , , and . ACL (Findings), page 4066-4083. Association for Computational Linguistics, (2023)MAVEN: A Massive General Domain Event Detection Dataset., , , , , , , , , and . EMNLP (1), page 1652-1671. Association for Computational Linguistics, (2020)Architecture of Grid Resource Allocation Management Based on QoS., and . GCC (2), volume 3033 of Lecture Notes in Computer Science, page 81-88. Springer, (2003)Adversarial Training for Weakly Supervised Event Detection., , , , and . NAACL-HLT (1), page 998-1008. Association for Computational Linguistics, (2019)CLEVE: Contrastive Pre-training for Event Extraction., , , , , , , , and . ACL/IJCNLP (1), page 6283-6297. Association for Computational Linguistics, (2021)MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation., , , , , , , , , and 2 other author(s). CoRR, (2023)OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding., , , , , , , and . EMNLP (Demos), page 508-517. Association for Computational Linguistics, (2023)Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction., , , , , , , , and . EMNLP, page 5876-5890. Association for Computational Linguistics, (2023)