  In this study, we explore an emerging research area of Continual Learning for
Temporal Sensitive Question Answering (CLTSQA). Previous research has primarily
focused on Temporal Sensitive Question Answering (TSQA), often overlooking the
unpredictable nature of future events. In real-world applications, it's crucial
for models to continually acquire knowledge over time, rather than relying on a
static, complete dataset. Our paper investigates strategies that enable models
to adapt to the ever-evolving information landscape, thereby addressing the
challenges inherent in CLTSQA. To support our research, we first create a novel
dataset, divided into five subsets, designed specifically for various stages of
continual learning. We then propose a training framework for CLTSQA that
integrates temporal memory replay and temporal contrastive learning. Our
experimental results highlight two significant insights: First, the CLTSQA task
introduces unique challenges for existing models. Second, our proposed
framework effectively navigates these challenges, resulting in improved
performance.
