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Related Questions
- How can pre-trained language models serve as a starting point for text classification tasks to leverage knowledge from large text corpora?
- What are some effective ways to fine-tune a pre-trained model to adapt to a specific text classification task and mitigate the effects of domain shift?
- Can multi-task learning be used to alleviate the issue of domain shift in text classification, and if so, how does it work?
- What role do features like word embeddings play in mitigating the effects of domain shift in text classification?
- Are there any techniques or tools available to automatically or semi-automatically transfer learned knowledge from one task or domain to another for text classification?
- How can data augmentation, such as using out-of-domain data or generative models, be leveraged to improve the generalization of text classification models?
- Are there any specific metrics or benchmarks that can be used to evaluate the effectiveness of transfer learning in mitigating domain shift in text classification?
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