Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. Compared to conventional neural approaches, instead of performing point estimation to find the best set parameters, the proposed model infers the parameters' posterior distribution directly, enhancing the model's capability to encode and express uncertainty about the predictions. Experimental results on the three widely used datasets show that Bayesian-Trans outperforms existing approaches for event temporal relation extraction. We additionally present detailed analyses on uncertainty quantification, comparison of priors, and ablation studies, illustrating the benefits of the proposed approach.

翻译：现有的事件间时序关系抽取模型缺乏一种系统化的方法来融合外部知识。本研究提出Bayesian-Trans——一种基于贝叶斯学习的方法，该方法将时序关系表示建模为潜在变量，并通过贝叶斯推理和平移函数推断其取值。与传统的神经网络方法不同，所提模型不是通过点估计寻找最优参数集，而是直接推断参数的后验分布，从而增强了模型对预测结果不确定性的编码与表达能力。在三个广泛使用的数据集上的实验结果表明，Bayesian-Trans在事件时序关系抽取任务上优于现有方法。我们还提供了不确定性量化、先验比较及消融研究的详细分析，进一步说明了所提出方法的优势。