Scattered Hypothesis Generation for Open-Ended Event Forecasting (2026.findings-acl)
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| Challenge: | Existing methods for event forecasting focus on the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. |
| Approach: | They propose a reinforcement learning framework that optimizes inclusiveness and diversity of the hypothesis by integrating validity-gated score into the overall objective. |
| Outcome: | The proposed framework outperforms baselines on two real-world benchmark datasets. |
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Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis
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| Challenge: | Open-ended text generation tasks require models to generate coherent continuation given limited preceding context. |
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Towards Generative Event Factuality Prediction (2023.findings-acl)
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| Challenge: | Existing methods for event factuality prediction focus on author's presentation of factuity . a novel end-to-end generative task is proposed to predict event factuality holders, targets, and their associated factual values. |
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