Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Abstract

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.

WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation

“…The basic idea for domain adaptation is to learn domain-invariant representations which generalize across the domains. To achieve this, the most prevailing method Domain Adversarial Neural Network (DANN) (Ganin et al, 2016;Qu et al, 2019;Xue et al, 2020) introduces a domain classifier and uses adversarial training to make the features unable to discriminate between source and target domains. This method has been applied to many NLP tasks.…”

Wasserstein Selective Transfer Learning for Cross-domain Text Mining

“…Different from several domain adaption methods proposed for sentiment classification (Qu et al, 2019b;Du et al, 2020;Xue et al, 2020;Zhang et al, 2019;He et al, 2018) where the labels in the target domain are not available, both the source and target labels are available in our transfer learning setting. In our setting, we seek to leverage data-sufficient domains to help target domains with less sufficient data labels.…”

A Novel Method for Reducing Overhead of Training Sentiment Analysis Network

“…They proposed that the smaller emotion granularity, the more necessary it is to filter important emotions in context. In 2020, Qianming Xue, Wei Zhang et al proposed a domainrecognizer, which uses two feature extractors to detect features of invariant sentiment [21]. Tomoki Ito, Kota Tsubouchi et al proposed an interpretable neural network lexical initialization learning method, judging primitive-level word emotions out of contextfirstly and judging global emotions [22].…”