提取还是猜测？提高事件时态关系抽取的可信度
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
摘要：
在本文中，我们试图从两个角度提高TempRel提取模型的可信度。第一个视角是基于上下文描述的真实抽取。为了实现这一目标，我们建议进行反事实分析，以减弱两种重要类型的训练偏差的影响：事件触发偏差和频繁标签偏差。我们还将时态信息添加到事件表征中，以明确强调上下文描述。第二个观点是提供适当的不确定性估计，并且当文本中没有描述关系时避免提取。通过在模型预测的分类分布上对Dirichlet先验进行参数化，我们改进了模型对正确性似然的估计，并使TempRel预测更具选择性。我们还采用温度缩放来重新校准模型的置信度后，偏差缓解。通过对MATRES、MATRES-DS和TDDiscourse的实验分析，我们证明了与SOTA方法相比，我们的模型更忠实地提取TempRel和时间线，特别是在分布偏移的情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
