Document-level relation extraction (DocRE) aims to determine which relations hold between a given entity pair within a document. As a multi-label classification task, the most commonly adopted paradigm introduces a learnable threshold to distinguish positive and negative classes for an entity pair. Under this paradigm, existing losses decouple the optimization into independent positive and negative losses, which interact solely with a shared threshold. This leads to two inherent limitations: ( i ) threshold instability caused by conflicting gradient updates from the decoupled losses; and ( ii ) optimization bias exacerbated by the severe imbalance between limited positive samples and abundant negative samples inherent in DocRE, which makes the model more likely to predict that no relation exists. To address these issues, we propose the A daptive-T hreshold G lobal L oss (ATGL). Unlike prior work, ATGL integrates positive, negative, and threshold optimization into a unified logit space and explicitly enforces ranking constraints on their contributions to the objective. Furthermore, ATGL incorporates an imbalance-aware optimization mechanism, thereby effectively addressing the severe class imbalance in DocRE. Our ATGL serves as a general optimization objective that can be readily applied to different DocRE models. Experiments on four datasets show that ATGL outperforms other DocRE losses and achieves state-of-the-art results, while consistently improving the performance of existing DocRE models. Code is available at https: //github.com/xhm-code/ATGL .
Huangming Xu, Fu Zhang, Zhixuan Yang et al.· Annual Meeting of the Associ...· 0 citations
Structurally extracting information from mixed-script documents that interleave standard text with open, compositional symbol systems is challenging for both optical character recognition (OCR) and vision–language models (VLMs). This difficulty is epitomized by Jianzi Pu—the ancient Guqin tablature. Unlike closed-set scripts, Jianzi glyphs are formed via infinite compositional rules and are densely integrated with Hanzi text, demanding a model that can simultaneously perform script discrimination, layout parsing, and structural transcription. We propose JZ-Tab, the first framework dedicated to the automated recognition of Jianzi Pu, which functions as an end-to-end structured visual information extraction system for mixed-script documents. Unlike traditional pipelines, JZ-Tab generates layout-aware markup directly from full-page images, bypassing the need for pre-segmentation. Specifically, to overcome the total absence of large-scale annotated datasets, we develop a novel, scalable synthetic-to-real pipeline that constructs layout-consistent pages from canonicalized glyph inventories. Furthermore, to capture the unique action-oriented semantics of the tablature, we introduce music-structured generation, injecting sequential regularities derived from symbolic music logic into the learning process. Finally, we train a VLM for direct page-to-markup generation. Evaluated zero-shot on authentic historical manuscripts, JZ-Tab improves F1 by +40.10 over the strongest generic VLM baselines, highlighting its potential for large-scale automated digitization of historical Guqin manuscripts and open, compositional symbol systems.
Zehan Li, Fu Zhang, Zhijun Liu et al.· Proceedings of the 32nd ACM...· 0 citations