Papers by Xiangnan Chen

2 papers
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document (2023.findings-emnlp)

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Challenge: Existing methods focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together.
Approach: They propose a Visual Relation Extraction framework that generates relation predictions on entity pairs extracted from scanned images and incorporates global structural knowledge into the representations of the entities.
Outcome: The proposed framework outperforms existing methods in fine-tuning setting and yields stronger data-efficient performance in the low-resource setting.
AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)

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Challenge: Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models.
Approach: They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories .
Outcome: The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification.

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