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Related Questions
- How do contextual models struggle to capture nuanced idiomatic expressions, and what are the implications for natural language processing?
- What are the key differences between idiomatic language and literal language, and how can fine-tuning models account for these differences?
- What are some common pitfalls in fine-tuning contextual models for idiomatic language, such as overfitting or underfitting, and how can they be avoided?
- How can the use of external knowledge graphs or lexical resources improve the performance of contextual models on idiomatic language?
- What role do contextual models' attention mechanisms play in capturing idiomatic language, and how can they be optimized for better performance?
- Can transfer learning from tasks such as sentiment analysis or question answering improve the performance of contextual models on idiomatic language?
- What are some best practices for fine-tuning contextual models for idiomatic language, such as selecting the right dataset or hyperparameters?
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