Papers with uncertainty reduction

3 papers
Foreseeing the Benefits of Incidental Supervision (2021.emnlp-main)

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Challenge: Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
Approach: They propose a unified PAC-Bayesian motivated informativeness measure that characterizes the uncertainty reduction provided by incidental supervision signals.
Outcome: The proposed measure quantifies the value added by incidental supervision signals to sequence tagging tasks.
Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation (2020.acl-main)

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Challenge: Existing studies on suspense have only sporadically been used in story generation systems.
Approach: They propose a hierarchical language model that computes surprise and uncertainty reduction over story representations and annotated short stories.
Outcome: The proposed model can predict suspense over story representations or probability distributions, and predicts suspensity in movie synopses.
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)

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Challenge: Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data.
Approach: They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies.
Outcome: The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives.

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