Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
| Outcome: | The proposed model outperforms existing methods on multi-domain sentiment analysis significantly. |
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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. |
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| Challenge: | a key roadblock is application to new domains, unseen in training. |
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Cross-Domain Sentiment Classification using Semantic Representation (2022.findings-emnlp)
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| Challenge: | Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains. |
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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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Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
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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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