论文标题
有效的时间关系提取的远处监督
Effective Distant Supervision for Temporal Relation Extraction
论文作者
论文摘要
在新领域训练时间关系提取模型的主要障碍是缺乏多样化，高质量的例子以及收集更多的挑战。我们提出了一种自动收集暂时关系遥远的例子的方法。我们刮擦并自动标记事件对，在文本中显式将时间关系显式，然后掩盖这些明确的提示，迫使在此数据上训练的模型以学习其他信号。我们证明，预先训练的变压器模型能够从零射击和少量设置中的人类宣传的基准转移到人宣传的基准，并且掩盖方案对于改善概括很重要。
A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where the temporal relations are made explicit in text, then mask out those explicit cues, forcing a model trained on this data to learn other signals. We demonstrate that a pre-trained Transformer model is able to transfer from the weakly labeled examples to human-annotated benchmarks in both zero-shot and few-shot settings, and that the masking scheme is important in improving generalization.
