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.

翻译：摘要：我们提出了一种投票驱动的半监督方法，用于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，我们的伪标签具有极高的准确率和均衡的覆盖率。在时间常识问答任务中，实验结果显示，仅使用400个事件的伪示例，我们就能达到与现有基于BERT的弱监督方法（需大量训练示例）相当的性能。与RoBERTa基线相比，我们提出的最佳方法在精确匹配指标上实现了7%的提升，达到了当前最优性能。