Papers with rule-based module

2 papers
Fully Statistical Neural Belief Tracking (P18-2)

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Challenge: Existing framework for a dialogue state tracking model requires an expensive manual retuning step .
Approach: They propose to improve existing NBT model by removing a manual retuning step . they propose two different statistical update mechanisms to improve model performance .
Outcome: The proposed model achieves competitive performance and provides a robust framework for building resource-light DST models.
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases (2024.eacl-long)

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Challenge: Reasoning over Commonsense Knowledge Bases (CSKBs) is a way to acquire new commonsense knowledge based on reference knowledge in original CSKB and external prior knowledge.
Approach: They propose a plugin to provide and check explicit relational constraints over prompting techniques.
Outcome: The proposed method improves on existing prompting techniques and CSKB reasoning.

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