域自适应因果编码器
Domain Adaptative Causality Encoder
摘要：
目前的方法，主要是基于单个事件之间的低层次关系的提取是有限的公开可用的标记数据的短缺。因此，当应用于在训练时不存在标记数据的分布不同的域时，所得到的模型表现不佳。为了克服这一限制，在本文中，我们利用依赖树和对抗学习的特性来解决自适应因果关系识别和定位的任务。使用自适应这个术语是因为训练和测试数据来自两个分布不同的数据集，据我们所知，这项工作是第一次解决。此外，我们提出了一个新的因果关系数据集，即MedCaus，它集成了文本中的所有类型的因果关系。我们在四个不同的基准因果关系数据集上的实验证明了我们的方法在现有基线上的优越性，在从文本中识别和定位因果关系的任务上，最多可提高7%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
