Cross-Domain Sentiment Classification with Target Domain Specific Information (P18-1)
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| Challenge: | Existing methods for sentiment classification focus on learning domain-invariant representations . few of them pay attention to domain-specific information, which should also be informative. |
| Approach: | They propose a method to extract domain specific and invariant representations and train a classifier on each of them. |
| Outcome: | The proposed model can achieve better performance than state-of-the-art methods. |
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| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
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| Challenge: | Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains. |
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Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (D19-1)
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| Challenge: | Existing approaches to cross-domain sentiment classification focus on domain-invariant representations, but few focus on the domain-specific information. |
| Approach: | They propose to distill domain-invariant sentiment features with an orthogonal domain-dependent task . the orthogonalist task is built on the aspects varying widely in different domains . |
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Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis (2020.acl-main)
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| Challenge: | Cross-domain sentiment classification requires large amounts of labeled data. |
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Domain Adaptation for Sentiment Analysis Using Robust Internal Representations (2023.findings-emnlp)
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| Challenge: | Cross-domain sentiment analysis methods reduce the domain gap by training generalizable classifiers for each domain . large interclass margins in source domain help to reduce the effect of "domain shift" in the target domain. |
| Approach: | They propose a domain adaptation method which induces large margins between data representations that belong to different classes in an embedding space. |
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Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Cross-domain sentiment analysis is a hot topic in research and industry . domain-invariant representation learning (DIRL) is used to learn a feature representation across domains . but, when label distribution P(Y) shifts across domain, it degrades performance . |
| Approach: | They propose a domain-invariant representation learning framework to improve cross-domain sentiment analysis performance. |
| Outcome: | The proposed model is easy to transfer existing models to the proposed model. |