  In this paper, we seek to improve the faithfulness of TempRel extraction
models from two perspectives. The first perspective is to extract genuinely
based on contextual description. To achieve this, we propose to conduct
counterfactual analysis to attenuate the effects of two significant types of
training biases: the event trigger bias and the frequent label bias. We also
add tense information into event representations to explicitly place an
emphasis on the contextual description. The second perspective is to provide
proper uncertainty estimation and abstain from extraction when no relation is
described in the text. By parameterization of Dirichlet Prior over the
model-predicted categorical distribution, we improve the model estimates of the
correctness likelihood and make TempRel predictions more selective. We also
employ temperature scaling to recalibrate the model confidence measure after
bias mitigation. Through experimental analysis on MATRES, MATRES-DS, and
TDDiscourse, we demonstrate that our model extracts TempRel and timelines more
faithfully compared to SOTA methods, especially under distribution shifts.
