Abstract
In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra noise, leading to interference with the model's judgment of the long-distance dependencies between events. To address these issues, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. We first present a distance-aware graph structure utilizing Graph Attention Network(GAT) to assist the LLMs in capturing long-distance dependency features. Additionally, we design a temporal feature learning paradigm based on soft inference to augment the identification of relations with a short-distance proximity band, which supplements the probabilistic information generated by LLMs into the multi-head attention mechanism. Since the global feature can be captured effectively, our framework substantially enhances the performance of minority relation classes and improves the overall learning ability. Experiments on two publicly available datasets, TB-Dense and MATRES, demonstrate that our approach achieves state-of-the-art (SOTA) performance.
Abstract (translated)
在自然语言处理（NLP）领域，事件时间关系抽取（ETRE）旨在识别两个事件之间的时间关系。先前的研究已经注意到了语言模型对于ETRE的重要性。然而，小型语言模型（SLM）有限的预训练知识限制了它们处理不平衡分类数据集中少数类别的能力。对于大型语言模型（LLM），研究人员采用手动设计的提示或指令，这可能会引入额外的噪声，从而干扰模型对事件之间长距离依赖关系的判断。 为了解决这些问题，我们提出了基于LLM的GDLLM方法，这是一种全局距离感知建模方法。首先，我们提出了一种利用图注意力网络（GAT）的距离感知图形结构来帮助LLM捕捉长时间依赖特征。此外，我们设计了一个基于软推理的时间特性学习范式，以增强对具有短距离接近度带的关系的识别，并将由LLMs生成的概率信息补充到多头注意机制中。由于可以有效地捕获全局特征，我们的框架显著提升了少数关系类别的性能并改进了整体学习能力。 在两个公开数据集TB-Dense和MATRES上的实验表明，我们提出的方法达到了最先进的（SOTA）性能。
URL
https://arxiv.org/abs/2508.20828
