COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction

2025-03-18

AI Summary

Document-level relation extraction (DocRE) suffers from severe label noise and extreme sparsity of positive instances, leading to biased model optimization and poor generalization. To address these challenges, we propose COMM, an end-to-end robust framework centered on a novel “centralized margin maximization” mechanism. COMM employs instance-aware reasoning to dynamically integrate instance difficulty and relational distribution information, adaptively modulating the classification margin between predicted logits and decision thresholds. It further introduces a distribution-aware loss function to mitigate the adverse effects of noisy and sparse supervision. Evaluated on low-quality annotated datasets, COMM achieves over 10% F1 improvement compared to strong baselines, demonstrating substantial robustness against both label noise and positive-instance sparsity. The framework establishes an interpretable and generalizable paradigm for robust DocRE learning.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Safety and Robustness
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Document-level relation extraction (DocRE) is the process of identifying and extracting relations between entities that span multiple sentences within a document. Due to its realistic settings, DocRE has garnered increasing research attention in recent years. Previous research has mostly focused on developing sophisticated encoding models to better capture the intricate patterns between entity pairs. While these advancements are undoubtedly crucial, an even more foundational challenge lies in the data itself. The complexity inherent in DocRE makes the labeling process prone to errors, compounded by the extreme sparsity of positive relation samples, which is driven by both the limited availability of positive instances and the broad diversity of positive relation types. These factors can lead to biased optimization processes, further complicating the task of accurate relation extraction. Recognizing these challenges, we have developed a robust framework called extit{ extbf{COMM}} to better solve DocRE. extit{ extbf{COMM}} operates by initially employing an instance-aware reasoning method to dynamically capture pertinent information of entity pairs within the document and extract relational features. Following this, extit{ extbf{COMM}} takes into account the distribution of relations and the difficulty of samples to dynamically adjust the margins between prediction logits and the decision threshold, a process we call Concentrated Margin Maximization. In this way, extit{ extbf{COMM}} not only enhances the extraction of relevant relational features but also boosts DocRE performance by addressing the specific challenges posed by the data. Extensive experiments and analysis demonstrate the versatility and effectiveness of extit{ extbf{COMM}}, especially its robustness when trained on low-quality data (achieves extgreater 10% performance gains).

Problem

• Addresses errors in labeling due to DocRE complexity.
• Mitigates bias from sparse positive relation samples.
• Enhances relation extraction with dynamic margin adjustment.

Innovation

• Instance-aware reasoning for entity pair information
• Concentrated Margin Maximization for dynamic adjustment
• Robust framework for low-quality data training