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Related Questions
- Can the fixed-size contextualization window be dynamically adjusted based on the input sequence or task type?
- How can the model's adaptability to changes in language register or tone be improved using a sliding window approach?
- What are some techniques for extending the fixed-size contextualization window to capture longer-range dependencies or nuances in language?
- Can the model's contextualization window be made more flexible by incorporating attention mechanisms or other adaptive weighting schemes?
- How can the model's performance be evaluated in terms of its ability to adapt to changes in language register or tone?
- What are some potential trade-offs between increasing the size of the contextualization window and the model's computational efficiency or training time?
- Can the model's contextualization window be made more robust to out-of-vocabulary words or rare language phenomena by incorporating techniques such as subword modeling or character-level encoding?
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