Why does a statistical language model using only character-level information struggle more to generate syntactically correct poetry with strict iambic pentameter than one trained on word-level data?

💡 Explanation

Iambic pentameter imposes constraints based on higher-level syntactic units and phrase structures. A character model relies on sequential dependencies and is therefore less capable of learning these hierarchical relationships, rather than a word-level model that captures phrase-level patterns, because it has explicit information.

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