Papers with MUC

3 papers
Triad-based Neural Network for Coreference Resolution (C18-1)

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Challenge: Entity coreference resolution aims to identify mentions that refer to the same entity.
Approach: They propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution.
Outcome: The proposed system generates affinity scores between mentions for coreference resolution.
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)

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Challenge: Existing systems for coreference resolution are difficult because of their long coreferent chains.
Approach: They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system .
Outcome: The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task.
Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)

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Challenge: Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse.
Approach: They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks.
Outcome: The proposed framework achieves superior privacy-utility trade-off compared to strong baselines.

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