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Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction

2024-05-16 17:48:21
Jianhao Chen, Haoyuan Ouyang, Junyang Ren, Wentao Ding, Wei Hu, Yuzhong Qu

Abstract

Facts extraction is pivotal for constructing knowledge graphs. Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction. In this paper, we specifically address the extraction of temporal facts from natural language text. Previous studies fail to handle the challenge of establishing time-to-fact correspondences in complex sentences. To overcome this hurdle, we propose a timeline-based sentence decomposition strategy using large language models (LLMs) with in-context learning, ensuring a fine-grained understanding of the timeline associated with various facts. In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results. To this end, we introduce TSDRE, a method that incorporates the decomposition capabilities of LLMs into the traditional fine-tuning of smaller pre-trained language models (PLMs). To support the evaluation, we construct ComplexTRED, a complex temporal fact extraction dataset. Our experiments show that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and ComplexTRED datasets.

Abstract (translated)

事实提取对于构建知识图谱至关重要。最近,对于下游任务中不断增加的时间性事实需求,导致出现了时间性事实提取任务。在本文中,我们重点讨论自然语言文本中提取时间性事实的问题。之前的研究未能解决在复杂句子中建立时间性事实的时间挑战。为了克服这一障碍,我们提出了一个基于时间轴的句子分解策略,使用大型语言模型(LLMs)进行预训练,确保对与各种事实相关的时间轴有细粒度的理解。此外,我们评估了LLMs的直接时间性事实提取性能,并得到不满意的结果。为此,我们引入了TSDRE,一种将LLM的分解能力融入对较小预训练语言模型(PLM)传统微调的方法。为了支持评估,我们构建了复杂的时间性事实提取数据集ComplexTRED。我们的实验结果表明,TSDRE在HyperRED-Temporal和ComplexTRED数据集上均取得了最先进的成果。

URL

https://arxiv.org/abs/2405.10288

PDF

https://arxiv.org/pdf/2405.10288.pdf


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