Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction

Understand

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.