Abstract
Cross-domain sentiment classification uses useful information of the source domain to promote the classification accuracy of the target domain. Although previous approaches consider the effects of aspect information of the sentences, they lack the mechanism of syntactic constraints which may mistakenly assign irrelevant words to aspects. In this paper, we propose Dual Adversarial Network based on BERT (DAN-BERT), which can better transfer sentiment across domains by jointly learning the representation of sentences and aspect-based syntax. Specifically, DAN-BERT extracts the common features at the sentence level and aspect-based syntax level by adversarial training. We learn the features of aspect-based syntax by building Graph Convolutional Network over the dependency tree of a sentence. Experiments on the four datasets show that Dual Adversarial Network based on BERT outperforms state-of-the-art methods.
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Acknowledgments
This paper is Supported by National Key Research and Development Program of China under Grant No.2017YFB0803003 and National Science Foundation for Young Scientists of China (Grant No. 61702507).
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Zhang, S., Bai, X., Jiang, L., Peng, H. (2021). Dual Adversarial Network Based on BERT for Cross-domain Sentiment Classification. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_44
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