Papers with cross-domain generalizable classifiers
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. |
| Outcome: | The proposed method reduces the domain gap by training cross-domain generalizable classifiers . large interclass margins in the source domain help reduce the effect of "domain shift" the proposed method is available in the u.s. |