  Cross-domain sentiment classification (CDSC) is an importance task in domain
adaptation and sentiment classification. Due to the domain discrepancy, a
sentiment classifier trained on source domain data may not works well on target
domain data. In recent years, many researchers have used deep neural network
models for cross-domain sentiment classification task, many of which use
Gradient Reversal Layer (GRL) to design an adversarial network structure to
train a domain-shared sentiment classifier. Different from those methods, we
proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which
alternately trains a generator and a discriminator in order to produce a
document representation which is sentiment-distinguishable but
domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated
Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a
sentence into the document representation. In addition, the HAGAN model use
hierarchical attention mechanism to optimize the document representation and
automatically capture the pivots and non-pivots. The experiments on Amazon
review dataset show the effectiveness of HAGAN.
