Papers by Jacob Gardner
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)
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| Challenge: | Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts. |
| Approach: | They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias. |
| Outcome: | The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text . |