Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification

Mengting Hu, Yike Wu, Shiwan Zhao, Honglei Guo, Renhong Cheng, Zhong Su


Abstract
Cross-domain sentiment classification has drawn much attention in recent years. Most existing approaches focus on learning domain-invariant representations in both the source and target domains, while few of them pay attention to the domain-specific information. Despite the non-transferability of the domain-specific information, simultaneously learning domain-dependent representations can facilitate the learning of domain-invariant representations. In this paper, we focus on aspect-level cross-domain sentiment classification, and propose to distill the domain-invariant sentiment features with the help of an orthogonal domain-dependent task, i.e. aspect detection, which is built on the aspects varying widely in different domains. We conduct extensive experiments on three public datasets and the experimental results demonstrate the effectiveness of our method.
Anthology ID:
D19-1558
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5559–5568
Language:
URL:
https://aclanthology.org/D19-1558
DOI:
10.18653/v1/D19-1558
Bibkey:
Cite (ACL):
Mengting Hu, Yike Wu, Shiwan Zhao, Honglei Guo, Renhong Cheng, and Zhong Su. 2019. Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5559–5568, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (Hu et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1558.pdf