Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis
Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, Jianxin Liao
Sentiment Analysis, Stylistic Analysis, and Argument Mining Long Paper
Session 7A: Jul 7
(08:00-09:00 GMT)
Session 8A: Jul 7
(12:00-13:00 GMT)
Abstract:
Cross-domain sentiment classification aims to address the lack of massive amounts of labeled data. It demands to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. In this paper, we investigate how to efficiently apply the pre-training language model BERT on the unsupervised domain adaptation. Due to the pre-training task and corpus, BERT is task-agnostic, which lacks domain awareness and can not distinguish the characteristic of source and target domain when transferring knowledge. To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task. The post-training procedure will encourage BERT to be domain-aware and distill the domain-specific features in a self-supervised way. Based on this, we could then conduct the adversarial training to derive the enhanced domain-invariant features. Extensive experiments on Amazon dataset show that our model outperforms state-of-the-art methods by a large margin. The ablation study demonstrates that the remarkable improvement is not only from BERT but also from our method.
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