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Related Questions
- How does the use of entities in attention mechanisms differ from self-attention mechanisms?
- What role do entities play in modifying the context-dependent calculation in entity-based attention models compared to self-attention mechanisms?
- What are some scenarios in NLP and text generation tasks where the distinction between entity-based and self-attention becomes apparent?
- Are there applications of entity-based attention beyond document-level summarization tasks for which it's optimized for?
- Do entity-based attention and self-attention interact during contextualized feature representations across tokens in their input data in different machine learning pipelines and architectures?
- Would you contrast how dynamic multi-head attentions vary according to types in context entity-based mechanisms in specific attention architectures
- How entity-based attention distinguishes itself through incorporating richer relational information or global properties beyond local information as contrasted with local contextualization that can be shared between heads, as per the local attentions applied within and among tokens for both, such as position-based relative weights
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