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.

我们提出了一种基于投票的半监督方法，自动获取事件的典型持续时间并将其用作伪标签数据。人工评估表明，我们的伪标签表现出令人惊讶的高准确性和平衡覆盖率。在时间常识问答任务中，实验结果显示，仅使用 400 个事件的伪样本，我们的表现可与现有的基于 BERT 的弱监督方法相媲美，而这些方法需要大量的训练样本。与 RoBERTa 基线相比，我们的最佳方法在准确匹配方面实现了最新的技术水平，提高了 7%。