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QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.

, , , , and . NAACL-HLT, page 535-546. Association for Computational Linguistics, (2021)

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Reasoning about Actions and State Changes by Injecting Commonsense Knowledge., , , , , and . CoRR, (2018)COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs., , , , , , and . CoRR, (2020)Exploiting Structural and Semantic Context for Commonsense Knowledge Base Completion., , , and . CoRR, (2019)QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering., , , , and . NAACL-HLT, page 535-546. Association for Computational Linguistics, (2021)ConVQG: Contrastive Visual Question Generation with Multimodal Guidance., , , , , and . AAAI, page 4207-4215. AAAI Press, (2024)Instruction-tuning Aligns LLMs to the Human Brain., , , , and . CoRR, (2023)The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics., , , , , , , , , and 45 other author(s). CoRR, (2021)Dynamic Knowledge Graph Construction for Zero-shot Commonsense Question Answering., and . CoRR, (2019)The Amazing World of Neural Language Generation., , , and . EMNLP (Tutorial Abstracts), page 37-42. Association for Computational Linguistics, (2020)WIQA: A dataset for "What if..." reasoning over procedural text., , , , and . EMNLP/IJCNLP (1), page 6075-6084. Association for Computational Linguistics, (2019)