论文标题
反事实检测符合转移学习
Counterfactual Detection meets Transfer Learning
论文作者
论文摘要
我们可以将反事实视为属于话语结构和语义的领域，这是自然语言理解的核心领域，在本文中，我们介绍了一种解决反事实检测的方法，以及对反面陈述的索引和反事实陈述的索引。尽管转移学习已经应用于多个NLP任务，但它具有在新的任务中表现出色的特征。我们表明，检测反事实是一项简单的二进制分类任务，由于有一个带注释的培训数据集，可以最少适应已经存在的模型体系结构，可以实现，并且我们引入了新的端到端管道，以处理动物和之后作为实体识别任务，从而将其调整为Token分类。
We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements. While Transfer learning is already being applied to several NLP tasks, It has the characteristics to excel in a novel number of tasks. We show that detecting Counterfactuals is a straightforward Binary Classification Task that can be implemented with minimal adaptation on already existing model Architectures, thanks to a well annotated training data set,and we introduce a new end to end pipeline to process antecedents and consequents as an entity recognition task, thus adapting them into Token Classification.
