Multi-domain sentiment classification deals with the scenario where labeled data exists for multiple domains but insufficient for training effective sentiment classifiers that work across domains. Thus, fully exploiting sentiment knowledge shared across domains is crucial for real world applications. While many existing works try to extract domain-invariant features in high-dimensional space, such models fail to explicitly distinguish between shared and private features at text-level, which to some extent lacks interpretablity. Based on the assumption that removing domain-related tokens from texts would help improve their domain-invariance, we instead first transform original sentences to be domain-agnostic. To this end, we propose the BertMasker network which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts, and uses those masked words to form domain-aware sentence representations. Empirical experiments on a well-adopted multiple domain sentiment classification dataset demonstrate the effectiveness of our proposed model on both multi-domain sentiment classification and cross-domain settings, by increasing the accuracy by 0.94% and 1.8% respectively. Further analysis on masking proves that removing those domain-related and sentiment irrelevant tokens decreases texts' domain distinction, resulting in the performance degradation of a BERT-based domain classifier by over 12%.

本文利用BertMasker网络，首先将原始句子转换为与领域无关的语句，明确地对文本中的领域相关单词进行掩蔽，从中学习领域无关情感特征，并使用这些掩蔽单词形成领域感知句子表示，提高了多领域情感分类和跨领域设置的准确性。

通过掩盖非共享部分进行多领域情感分类的共享学习