  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.
