Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Attention Generative Adversarial Networks for Cross-domain
  Sentiment Classification   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.

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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th