  Event temporal reasoning aims at identifying the temporal relations between
two or more events from narratives. However, knowledge conflicts arise when
there is a mismatch between the actual temporal relations of events in the
context and the prior knowledge or biases learned by the model. In this paper,
we propose to detect knowledge-conflict examples in event temporal reasoning
using bias indicators, which include event relation prior bias, tense bias,
narrative bias, and dependency bias. We define conflict examples as those where
event relations are opposite to biased or prior relations. To mitigate
event-related knowledge conflicts, we introduce a Counterfactual Data
Augmentation (CDA) based method that can be applied to both Pre-trained
Language Models (PLMs) and Large Language Models (LLMs) either as additional
training data or demonstrations for In-Context Learning. Experiments suggest
both PLMs and LLMs suffer from knowledge conflicts in event temporal reasoning,
and CDA has the potential for reducing hallucination and improving model
performance.
