Abstract
Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses challenges for low-resource languages like Malaysian English. These challenges include the incorporation of local linguistic nuances and the risk of factual inaccuracies in LLM-generated data. This paper introduces Document-Level Zero-Shot Relation Extraction with Entity Side Information (DocZSRE-SI) to address limitations in the existing DocZSRE approach. The DocZSRE-SI framework leverages Entity Side Information, such as Entity Mention Descriptions and Entity Mention Hypernyms, to perform ZSRE without depending on LLM-generated synthetic data. The proposed low-complexity model achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. By utilizing Entity Side Information, DocZSRE-SI offers a robust and efficient alternative to error-prone, LLM-based methods, demonstrating significant advancements in handling low-resource languages and linguistic diversity in relation extraction tasks. This research provides a scalable and reliable solution for ZSRE, particularly in contexts like Malaysian English news articles, where traditional LLM-based approaches fall short.
Abstract (translated)
文档级零样本关系抽取（DocZSRE）的目标是在没有针对特定关系进行前期训练的情况下，预测文本文件中未见过的关系标签。现有的方法依赖于大型语言模型（LLMs）来生成用于未见标签的合成数据，这对于马来西亚英语等资源匮乏的语言来说存在挑战，这些挑战包括难以融入当地的语言细微差别以及由LLM生成的数据中的事实错误风险。 本文提出了一种名为文档级零样本关系抽取带实体侧面信息（DocZSRE-SI）的方法，以解决现有DocZSRE方法的限制。DocZSRE-SI框架利用了诸如实体提及描述和实体提及超类之类的实体侧面信息来进行零样本关系抽取，而无需依赖于LLM生成的合成数据。所提出的低复杂度模型相比基线模型和现有的基准，在宏平均F1分数上提高了11.6%。 通过使用实体侧面信息，DocZSRE-SI为基于LLMs的方法提供了一个稳健且高效的替代方案，表明在处理资源匮乏的语言以及关系抽取任务中的语言多样性方面取得了重大进展。这项研究提供了用于零样本关系抽取的可扩展和可靠解决方案，在马来西亚英语新闻文章等情境下尤其有效，其中传统的基于LLM的方法表现不佳。
URL
https://arxiv.org/abs/2601.07271
