  We propose a voting-driven semi-supervised approach to automatically acquire
the typical duration of an event and use it as pseudo-labeled data. The human
evaluation demonstrates that our pseudo labels exhibit surprisingly high
accuracy and balanced coverage. In the temporal commonsense QA task,
experimental results show that using only pseudo examples of 400 events, we
achieve performance comparable to the existing BERT-based weakly supervised
approaches that require a significant amount of training examples. When
compared to the RoBERTa baselines, our best approach establishes
state-of-the-art performance with a 7% improvement in Exact Match.
