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Related Questions
- Can subword tokenization approaches like WordPiece or BPE improve the detection of sarcasm and irony in contextual models?
- How do different subword tokenization techniques impact the ability of language models to recognize subtle tone and attitude in language?
- What are the implications of using character-level subword tokenization on the performance of models in detecting irony and sarcasm?
- Do subword tokenization approaches that focus on morphological features improve the understanding of nuanced language cues like sarcasm and irony?
- Can the choice of subword tokenization impact the generalizability of contextual models to out-of-domain data when it comes to recognizing sarcasm and irony?
- How does the use of subword tokenization affect the computational resources required for training and inference in sarcasm and irony detection?
- Are there any specific subword tokenization techniques that have been shown to be more effective than others in capturing the subtleties of human language, such as sarcasm and irony?
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