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    <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.</abstract>
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%0 Conference Proceedings
%T Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis
%A Du, Chunning
%A Sun, Haifeng
%A Wang, Jingyu
%A Qi, Qi
%A Liao, Jianxin
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F du-etal-2020-adversarial
%X 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.
%R 10.18653/v1/2020.acl-main.370
%U https://aclanthology.org/2020.acl-main.370
%U https://doi.org/10.18653/v1/2020.acl-main.370
%P 4019-4028

Markdown (Informal)

[Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis](https://aclanthology.org/2020.acl-main.370) (Du et al., ACL 2020)

• Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis (Du et al., ACL 2020)

ACL

• Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020. Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4019–4028, Online. Association for Computational Linguistics.