论文标题
通过深度对抗性相互学习改善域适应性情感分类
Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
论文作者
论文摘要
适应域的情感分类是指在标记的源域上进行培训，以很好地推断出未标记的目标域上的文档级别的情感。大多数现有的相关模型都涉及功能提取器和情感分类器，该特征提取器可用于从两个域中学习域的不变特征，而情感分类器仅在源域上进行训练以指导特征提取器。因此，他们缺乏使用目标域中情感极性的机制。为了通过从目标领域学习情感来改善域适应性的情感分类，我们设计了一种新型的深层对抗相互学习方法，涉及两组特征提取器，域歧视器，情感分类器和标签概率。域鉴别器使特征提取器能够获得域不变特征。同时，每个组中的标签专家通过分类器在同伴组中生成的情感预测来探索目标域的情绪极性，并指导自己组中特征提取器的学习。提出的方法以端到端的方式实现了两组的相互学习。多个公共数据集的实验表明我们的方法获得了最先进的性能，从而通过标签概率验证了相互学习的有效性。
Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. Most existing relevant models involve a feature extractor and a sentiment classifier, where the feature extractor works towards learning domain-invariant features from both domains, and the sentiment classifier is trained only on the source domain to guide the feature extractor. As such, they lack a mechanism to use sentiment polarity lying in the target domain. To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers. The domain discriminators enable the feature extractors to obtain domain-invariant features. Meanwhile, the label prober in each group explores document sentiment polarity of the target domain through the sentiment prediction generated by the classifier in the peer group, and guides the learning of the feature extractor in its own group. The proposed approach achieves the mutual learning of the two groups in an end-to-end manner. Experiments on multiple public datasets indicate our method obtains the state-of-the-art performance, validating the effectiveness of mutual learning through label probers.
