Document-level relation extraction aims to identify relationships between entities within a document. Current methods rely on text-based encoders and employ various hand-coded pooling heuristics to aggregate information from entity mentions and associated contexts. In this paper, we replace these rigid pooling functions with explicit graph relations by leveraging the intrinsic graph processing capabilities of the Transformer model. We propose a joint text-graph Transformer model, and a graph-assisted declarative pooling (GADePo) specification of the input which provides explicit and high-level instructions for information aggregation. This allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customizable pooling strategies. We extensively evaluate our method across diverse datasets and models, and show that our approach yields promising results that are comparable to those achieved by the hand-coded pooling functions.

翻译：文档级关系抽取旨在识别文档中实体间的关系。当前方法依赖基于文本的编码器，并采用各种手工编码的池化启发式方法，从实体提及及其相关上下文中聚合信息。本文通过利用Transformer模型的内在图处理能力，将这些僵化的池化函数替换为显式图关系。我们提出一种联合文本-图Transformer模型，以及一种图辅助声明式池化（GADePo）的输入规范，为信息聚合提供显式高层次指令。这使得池化过程可由领域特定知识或期望结果引导，但仍由Transformer学习，从而产生更灵活、可自定义的池化策略。我们在多种数据集和模型上进行了广泛评估，结果表明我们的方法取得了与手工编码池化函数相媲美的理想结果。