Abstract
We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations -- entropy minimization and self-ensemble bootstrapping -- to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.
Abstract (translated)
我们考虑跨域情感分类问题，其中要从源域学习情感分类器并将其推广到目标域。我们的方法明确地最小化了嵌入式特征空间中源和目标实例之间的距离。随着源和目标之间的差异被最小化，我们通过巩固半监督学习的思想来利用来自目标域的附加信息，为此，我们联合使用两个正则化 - 熵最小化和自整体自举 - 来合并用于分类器细化的未标记目标数据。我们的实验结果表明，所提出的方法可以更好地利用来自目标域的未标记数据，并在各种实验设置中实现相对于基线方法的实质性改进。
URL
https://arxiv.org/abs/1809.00530
