Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification (2022.starsem-1)
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| Challenge: | Existing models for class imbalanced labels learn domain-invariant representations across domains and evaluate primarily on class-balanced data. |
| Approach: | They propose an unsupervised domain adaptation approach that leverages feature variants and imbalanced labels across domains to learn robust representations. |
| Outcome: | The proposed method can learn robust domain-invariant representations and adapt classifiers on imbalanced classes over domains. |
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