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Related Questions
- How can I mitigate the over-attention phenomenon in transformer-based models, such as BERT and RoBERTa, to improve translation quality and reduce bias?
- What are some techniques to use in pre-training and fine-tuning to prevent over-attention on specific entities in neural machine translation models?
- Can you explain the concept of entity-specific attention and how it can lead to biased translations, and what strategies can be employed to address this issue?
- What is the relationship between attention mechanisms and the phenomenon of over-attention, and how can I design more robust attention mechanisms to avoid biased translations?
- How can I apply techniques from the field of attention analysis to identify and mitigate over-attention on specific entities in my machine translation model?
- What are some common pitfalls to watch out for when using attention-based models in machine translation, and how can I avoid them to ensure more accurate and unbiased translations?
- Can you provide some real-world examples of how over-attention on specific entities has led to biased translations, and how they were addressed in each case?
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