论文标题
域适应性因果关系编码器
Domain Adaptative Causality Encoder
论文作者
论文摘要
主要基于在各个事件之间提取低级关系的当前方法受到公开可用标记数据的短缺的限制。因此，当应用于训练时不存在标记数据的分布不同的域时，所得模型的性能很差。为了克服这一限制，在本文中，我们利用依赖树和对抗性学习的特征来解决自适应因果关系识别和本地化的任务。由于培训和测试数据来自两个分布不同的数据集，因此使用了“自适应”一词，据我们所知，这项工作是第一个解决的工作。此外，我们提出了一个新的因果关系数据集，即MEDCAU，该数据集整合了文本中的所有因果关系。我们对四个不同基准因果关系数据集进行的实验证明了我们方法比现有基准的优越性，最大提高了7％，在识别和本地化因果关系的任务中，我们的方法的优越性。
Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation. The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the first to address. Moreover, we present a new causality dataset, namely MedCaus, which integrates all types of causality in the text. Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.
