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Creators/Authors contains: "Rajaby_Faghihi, Hossein"

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  1. ; (, Association for Computational Linguistics)
    This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions’ prior probability, confidence (uncertainty), and the models’ expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets. 
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    Accepted Manuscript Full Text Available