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Related Questions
- Can you explain the concept of domain adaptation in NLP and its importance in transfer learning?
- How does domain adaptation handle the differences in language and terminology between source and target domains?
- What are some common domain adaptation techniques used in NLP tasks, such as text classification and sentiment analysis?
- In what ways can domain adaptation help to reduce the out-of-domain performance of machine learning models in NLP?
- What are some challenges and limitations of domain adaptation in NLP, and how can they be addressed?
- Can you describe a scenario where domain adaptation is particularly useful in NLP, such as adapting a model from one language to another?
- How does domain adaptation relate to other transfer learning techniques in NLP, such as multi-task learning and fine-tuning?
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