Papers with upstream model

2 papers
Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)

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Challenge: Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries.
Approach: They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time.
Outcome: The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE.
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning (2021.naacl-main)

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Challenge: PTLMs can exhibit biases against protected groups in a host of modeling tasks . but, fine-tuned LMs may propagate bias to downstream classifiers .
Approach: They propose to use upstream bias mitigation techniques to reduce bias on downstream tasks by fine-tuning an upstream model and applying it to a downstream model.
Outcome: The proposed model reduces bias on hate speech detection, toxicity detection and coreference resolution tasks over bias factors.

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