@inproceedings{hu-etal-2019-domain,
title = "Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification",
author = "Hu, Mengting and
Wu, Yike and
Zhao, Shiwan and
Guo, Honglei and
Cheng, Renhong and
Su, Zhong",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1558",
doi = "10.18653/v1/D19-1558",
pages = "5559--5568",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification
%A Hu, Mengting
%A Wu, Yike
%A Zhao, Shiwan
%A Guo, Honglei
%A Cheng, Renhong
%A Su, Zhong
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hu-etal-2019-domain
%X 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.
%R 10.18653/v1/D19-1558
%U https://aclanthology.org/D19-1558
%U https://doi.org/10.18653/v1/D19-1558
%P 5559-5568
Markdown (Informal)
[Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification](https://aclanthology.org/D19-1558) (Hu et al., EMNLP-IJCNLP 2019)
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.