一次性生成完整时间图谱，提升零样本事件时序抽取效率与一致性。
Beyond Pairwise: Global Zero-shot Temporal Graph Generation
- 单步生成全文档时间图谱，避免逐对分类的低效与不一致。
- 在OmniTemp数据集上超越现有零样本方法，接近有监督模型表现。
- 适合需要快速部署、无标注数据场景下的时序关系抽取任务。
时间关系抽取（TRE）是自然语言处理中的基础任务，旨在识别文档中事件间的时间关系。尽管大语言模型（LLMs）取得进展，其在TRE中的应用仍有限。现有方法多依赖于孤立的成对分类，导致计算效率低且生成的时间图谱缺乏全局一致性。本文提出一种新颖的零样本TRE方法，可一步生成文档完整的时序图谱，并通过时序约束优化进行修正，确保关系间的一致性。此外，我们构建了OmniTemp数据集，包含文档内所有目标事件对的完整标注。实验与分析表明，该方法在零样本场景下优于现有方法，性能可与有监督模型相媲美。
原文摘要 · Abstract (English)
Temporal relation extraction (TRE) is a fundamental task in natural language processing (NLP) that involves identifying the temporal relationships between events in a document. Despite the advances in large language models (LLMs), their application to TRE remains limited. Most existing approaches rely on pairwise classification, where event pairs are classified in isolation, leading to computational inefficiency and a lack of global consistency in the resulting temporal graph. In this work, we propose a novel zero-shot method for TRE that generates a document's complete temporal graph in a single step, followed by temporal constraint optimization to refine predictions and enforce temporal consistency across relations. Additionally, we introduce OmniTemp, a new dataset with complete annotations for all pairs of targeted events within a document. Through experiments and analyses, we demonstrate that our method outperforms existing zero-shot approaches and offers a competitive alternative to supervised TRE models.
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