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NIST TAC SM-KBP 2019 System Description: JHU/UR Framework., , , , , , , , , and 5 other author(s). TAC, NIST, (2019)Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training., , , and . CoRR, (2024)Calibrated Interpretation: Confidence Estimation in Semantic Parsing., and . CoRR, (2022)Language-guided Skill Learning with Temporal Variational Inference., , , , , , and . CoRR, (2024)The Curious Case of Control., and . EMNLP, page 11065-11076. Association for Computational Linguistics, (2022)Did You Mean...? Confidence-based Trade-offs in Semantic Parsing., and . EMNLP, page 2621-2629. Association for Computational Linguistics, (2023)Iterative Paraphrastic Augmentation with Discriminative Span Alignment., , , , and . Trans. Assoc. Comput. Linguistics, (2021)Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models., , and . CoRR, (2023)Soft Self-Consistency Improves Language Model Agents., , , and . CoRR, (2024)MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models., , , and . CoRR, (2024)