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Related Questions
- Can attention heads learn to focus on context-dependent subword representations for out-of-vocabulary words?
- How do attention heads handle morphological and orthographic features of out-of-vocabulary words in machine translation?
- Are there any strategies for incorporating pre-existing word embeddings or dictionaries to aid in handling out-of-vocabulary words with attention heads?
- Can attention heads learn to represent rare or out-of-vocabulary words as composites of multiple subwords?
- How do different attention head architectures and implementations handle out-of-vocabulary words in machine translation tasks?
- Are there any known trade-offs between model complexity and performance when using attention heads to handle out-of-vocabulary words?
- Can attention heads be combined with other techniques, such as subword modeling or external knowledge sources, to improve handling of out-of-vocabulary words?
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