Document-level relation extraction (DocRE) involves identifying relations
between entities distributed in multiple sentences within a document. Existing
methods focus on building a heterogeneous document graph to model the internal
structure of an entity and the external interaction between entities. However,
there are two drawbacks in existing methods. On one hand, anaphor plays an
important role in reasoning to identify relations between entities but is
ignored by these methods. On the other hand, these methods achieve
cross-sentence entity interactions implicitly by utilizing a document or
sentences as intermediate nodes. Such an approach has difficulties in learning
fine-grained interactions between entities across different sentences,
resulting in sub-optimal performance. To address these issues, we propose an
Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the
widely-used datasets demonstrate that our model achieves a new state-of-the-art
performance.

我们提出了一种基于指代辅助的框架用于文档级关系抽取任务，实验结果表明我们的模型取得了最先进的性能。

照应辅助的文档级关系抽取

Anaphor Assisted Document-Level Relation Extraction

Few-shot relation learning refers to infer facts for relations with a limited
number of observed triples. Existing metric-learning methods for this problem
mostly neglect entity interactions within and between triples. In this paper,
we explore this kind of fine-grained semantic meanings and propose our model
TransAM. Specifically, we serialize reference entities and query entities into
sequence and apply transformer structure with local-global attention to capture
both intra- and inter-triple entity interactions. Experiments on two public
benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the
effectiveness of TransAM.

本文介绍了一种基于序列化和 Transformer 结构的模型 TransAM，以捕捉三元组内部和跨三元组的实体交互，以解决 Few-shot 关系学习问题。在两个公共基准数据集 NELL-One 和 Wiki-One 上的实验表明了 TransAM 的有效性。