Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation classes, caused by language complexity and data sparsity. Further, these approaches and models are largely inaccessible to users who don't have direct access to large language models (LLMs) and/or infrastructure for supervised training or fine-tuning. Rule-based systems also struggle with implicit expressions. Apart from this, Real world financial documents such as various 10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose another challenge to rule-based systems in terms of longer and complex sentences. In this paper, we introduce a simple approach that consults training relations at test time through a nearest-neighbor search over dense vectors of lexico-syntactic patterns and provides a simple yet effective means to tackle the above issues. We evaluate our approach on REFinD and show that our method achieves state-of-the-art performance. We further show that it can provide a good start for human in the loop setup when a small number of annotations are available and it is also beneficial when domain experts can provide high quality patterns.

翻译：关系抽取（RE）借助预训练语言模型取得了显著进展。然而，现有关系抽取模型通常难以处理两类情况：由语言复杂性和数据稀疏性导致的隐含表达与长尾关系类别。此外，这些方法及模型对无法直接访问大型语言模型（LLM）或缺乏监督训练/微调基础设施的用户而言基本不可用。基于规则的系统同样难以应对隐含表达。除此之外，公开交易公司各类10-X报告（包括10-K、10-Q等）等真实金融文档中的长句与复杂句子，对基于规则的系统构成了另一挑战。本文提出一种简单方法，在测试阶段通过最近邻搜索对词汇-句法模式的稠密向量进行训练关系查询，从而以简洁有效的方式解决上述问题。我们在REFinD数据集上评估该方法，结果表明其达到了最先进性能。进一步实验证明，当仅有少量标注可用时，该方法能为"人在回路"设置提供良好起点；当领域专家能提供高质量模式时，该方法同样具有显著效益。