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Related Questions
- Can domain adaptation techniques such as adversarial training or multi-task learning improve the robustness of NLP models to out-of-domain data?
- How does the choice of adaptation method and hyperparameters impact the robustness of NLP models to out-of-domain data?
- What are the trade-offs between adaptation accuracy and robustness in NLP models, and how can they be optimized?
- Can domain adaptation techniques be used to adapt NLP models to new domains with limited labeled data?
- How does the quality and quantity of the adaptation data affect the robustness of NLP models to out-of-domain data?
- Can transfer learning and domain adaptation be combined to improve the robustness of NLP models to out-of-domain data?
- What are the key challenges in adapting NLP models to out-of-domain data, and how can they be addressed using domain adaptation techniques?
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