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Related Questions
- Can non-contextual models be pre-trained on large datasets and fine-tuned on specific task-oriented datasets to handle idiomatic language?
- How can researchers employ techniques like knowledge graph augmentation or multi-task learning to enhance the ability of non-contextual models to comprehend idiomatic expressions?
- What is the impact of using self-supervised learning methods like masking or permutation on improving non-contextual models' ability to capture idiomatic language patterns?
- Can non-contextual models be fine-tuned with task-specific datasets and external knowledge sources to mitigate their limitations in handling idiomatic language?
- How do contextual and non-contextual models compare in terms of their ability to handle idiomatic language, and what are the trade-offs between them?
- Can attention-based mechanisms be applied to non-contextual models to better capture the context and nuance of idiomatic language?
- What are the key differences in the architecture and design of non-contextual models, and how do they influence their ability to handle idiomatic language?
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