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Related Questions
- Can pre-training on large corpus with diverse linguistic styles help non-contextual models understand idioms and figurative language better?
- How can the use of word embeddings and semantic role labeling improve the representation of idioms and figurative language in non-contextual models?
- What role can machine learning techniques such as transfer learning and fine-tuning play in enhancing non-contextual models' understanding of idioms and figurative language?
- Can incorporating additional linguistic resources such as dictionaries and thesauri aid non-contextual models in recognizing and interpreting idioms and figurative language?
- How can the development of task-specific datasets and evaluation metrics contribute to the improvement of non-contextual models' performance on understanding idioms and figurative language?
- Can the incorporation of cognitive and linguistic theories of figurative language help inform the design and development of more effective non-contextual models?
- What are some potential limitations and challenges associated with improving non-contextual models' understanding of idioms and figurative language, and how can these be addressed?
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