Papers by Ximing Wen

2 papers
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification (2025.coling-main)

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Challenge: Existing models with black-box nature obscure decision-making process and lack interpretability.
Approach: They propose a multi-head graph attention-based prototypical network that uses a vector and prototypes to learn an interpretable prototypical representation.
Outcome: The proposed model achieves superior results without sacrificing the accuracy of the original black-box LMs.
Switching Heads and Softening Tokens: Turnkey Solutions to Visually Grounded Document QA (2026.findings-acl)

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Challenge: Document Question Answering lacks robust, end-to-end solutions capable of handling complex, multi-answer queries without reliance on ad-hoc processing.
Approach: They propose a single-head architecture where coordinates are represented as special tokens within the unified vocabulary.
Outcome: The proposed architectures improve visual grounding but lack spatial precision bound by discretization.

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