DAN：用于使姿态分类器适应新领域的双视图表示学习
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains
摘要：
我们解决的问题，有一个有限数量的注释的立场分类在一个新的领域，通过适应域外的分类与领域自适应。现有的方法通常在单个全局特征空间（或视图）中对齐不同的域，这可能无法完全捕获用于表达立场的语言的丰富性，从而导致对立场数据的适应性降低。在本文中，我们确定了两种主要类型的立场表达是语言上不同的，我们提出了一个量身定制的双视图适应网络（DAN），以适应这些跨域的表达。该模型首先在每个表达通道中学习一个单独的视图进行域转移，然后选择两个视图中最适合的部分进行最佳转移。我们发现，学习的视图功能可以更容易地对齐和更多的立场歧视，在任何一个或两个视图，导致更多的可转移的整体功能后，结合的意见。从广泛的实验结果表明，我们的方法可以提高国家的最先进的单视图方法在不同领域的立场数据匹配，它一贯改善这些方法在各种适应任务。
英文摘要：
We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation. Existing approaches often align different domains in a single, global feature space (or view), which may fail to fully capture the richness of the languages used for expressing stances, leading to reduced adaptability on stance data. In this paper, we identify two major types of stance expressions that are linguistically distinct, and we propose a tailored dual-view adaptation network (DAN) to adapt these expressions across domains. The proposed model first learns a separate view for domain transfer in each expression channel and then selects the best adapted parts of both views for optimal transfer. We find that the learned view features can be more easily aligned and more stance-discriminative in either or both views, leading to more transferable overall features after combining the views. Results from extensive experiments show that our method can enhance the state-of-the-art single-view methods in matching stance data across different domains, and that it consistently improves those methods on various adaptation tasks.
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。
