论文标题
提取事件时间关系提取的多尺度知识
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction
论文作者
论文摘要
事件时间关系提取（ETRE）至关重要，但具有挑战性。在话语中，事件对位于不同的距离或所谓的接近频段。有关事件对的时间顺序，其中在更遥远（即````long''''）或更少的遥控器（即``short''）接近频段的编码方式有所不同。 SOTA模型倾向于在位于短或较长的接近频段的事件上表现良好，但并非两者兼而有之。尽管如此，实际的自然文本包含所有类型的时间事件对。在本文中，我们介绍了Mulco：通过对比度学习来提取多尺度知识，这是一种知识共同依据的方法，该方法在多个事件对接近频段中共享知识，以提高所有类型的暂时数据集的性能。我们的实验结果表明，Mulco成功地整合了与短期和长度接近频段的时间推理有关的语言提示，并在几个ETRE基准数据集中实现了新的最新结果。
Event Temporal Relation Extraction (ETRE) is paramount but challenging. Within a discourse, event pairs are situated at different distances or the so-called proximity bands. The temporal ordering communicated about event pairs where at more remote (i.e., ``long'') or less remote (i.e., ``short'') proximity bands are encoded differently. SOTA models have tended to perform well on events situated at either short or long proximity bands, but not both. Nonetheless, real-world, natural texts contain all types of temporal event-pairs. In this paper, we present MulCo: Distilling Multi-Scale Knowledge via Contrastive Learning, a knowledge co-distillation approach that shares knowledge across multiple event pair proximity bands to improve performance on all types of temporal datasets. Our experimental results show that MulCo successfully integrates linguistic cues pertaining to temporal reasoning across both short and long proximity bands and achieves new state-of-the-art results on several ETRE benchmark datasets.
